** Combining different governmental datasets to prioritize fire department resources **
Fire departments across the United States spend considerable resources on educational and prevention campaigns to stop building fires before they happen. Moving from a shotgun approach to a targeted one that considers the likelihood of fire incidents could significantly improve the efficacy of these interventions. To do this, machine learning algorithms built on data from a wide variety of sources could vastly improve how we perform against randomness.
Using data provided by the city of Sioux Falls, this notebook walks through the steps of predicting fire risk for every address listed in the city with a random forest classifier algorithm written in Python, and trained on data coming from several different county and municipal departments.
# Data analysis and visualization
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Interactive maps
import folium
from folium.plugins import HeatMap
# Machine learning
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_curve, roc_auc_score, auc
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.pipeline import Pipeline
# Load the data into Python
fires_df = pd.read_csv('data/fires2.csv', parse_dates=[6])
parcels_df = pd.read_excel('data/parcels2.xlsx', parse_dates=True)
foreclosures_df = pd.read_excel('data/foreclosures.xlsx', parse_dates=True)
rent_reg_df = pd.read_excel('data/rental_registration_permits.xlsx', parse_dates=True)
utility_disconnects_df = pd.read_excel('data/utility_disconnects.xlsx', parse_dates=True)
code_cases_df = pd.read_excel('data/code_cases.xlsx', parse_dates = True)
minnehaha_tax_df = pd.read_excel('data/minnehaha_tax.xlsx', parse_dates = True)
lincoln_tax_df = pd.read_excel('data/lincoln_tax.xlsx', parse_dates = True)
crime_df = pd.read_excel('data/crime.xlsx', parse_dates = True)
C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided" C:\Users\User\Anaconda3\lib\site-packages\pandas\io\excel.py:520: UserWarning: The 'parse_dates=True' keyword of read_excel was provided without an 'index_col' keyword value. warn("The 'parse_dates=True' keyword of read_excel was provided"
fires_df.type.value_counts()
Building fire 1258 Passenger vehicle fire 639 Cooking fire, confined to container 244 Grass fire 198 Dumpster or other outside trash receptacle fire 183 Outside rubbish, trash or waste fire 124 Brush, or brush and grass mixture fire 94 Special outside fire, other 91 Outside equipment fire 80 Fire, other 71 Mobile property (vehicle) fire, other 71 Natural vegetation fire, other 70 Trash or rubbish fire, contained 64 Outside rubbish fire, other 57 Fire in mobile home used as fixed residence 56 Road freight or transport vehicle fire 35 Forest, woods or wildland fire 19 Off-road vehicle or heavy equipment fire 16 Fires in structures other than in a building 15 Outside storage fire 10 Cultivated grain or crop fire 7 Chimney or flue fire, confined to chimney or flue 7 Fire in portable building, fixed location 7 Camper or recreational vehicle (RV) fire 6 Outside gas or vapor combustion explosion 5 Fuel burner/boiler malfunction, fire confined 5 Commercial Compactor fire, confined to rubbish 3 Self-propelled motor home or recreational vehicle 3 Incinerator overload or malfunction, fire confined 3 Cultivated vegetation, crop fire, other 3 Fire in mobile property used as a fixed structure, other 3 Fire in motor home, camper, recreational vehicle 2 Outside mailbox fire 2 Cultivated trees or nursery stock fire 2 Water vehicle fire 2 Aircraft fire 2 Outside stationary compactor/compacted trash fire 1 Garbage dump or sanitary landfill fire 1 Construction or demolition landfill fire 1 Name: type, dtype: int64
# Create copys of DataFrames so if I mess something up I don't have to load up everything again
building_fires = fires_df[fires_df['id2']== 111].copy()
parcels = parcels_df.copy()
foreclosures = foreclosures_df.copy()
rent_reg = rent_reg_df.copy()
utility_disconnects = utility_disconnects_df.copy()
code_cases = code_cases_df.copy()
minnehaha_tax = minnehaha_tax_df.copy()
lincoln_tax = lincoln_tax_df.copy()
crime = crime_df.copy()
parcels.columns
Index(['TAG', 'COUNTYID', 'ADDRESS', 'OWNNAME1', 'OWNNAME2', 'OWNADDRESS', 'OWNCITY', 'OWNSTATE', 'OWNZIP', 'OWNZIP2', 'SQFT', 'ACREAGE', 'FRONTFOOT', 'LEGAL', 'ADDITION', 'ADDITIONNU', 'PARHOUSE', 'PARHALF', 'PARPR', 'PARSTREET', 'PARTYPE', 'PARPD', 'UNITNUM', 'ACTIVITY', 'LANDUSE', 'NUMUNITS', 'COUNTY', 'LegalStart', 'GlobalID', 'created_us', 'created_da', 'last_edite', 'last_edi_1', 'ADDITIONPR', 'PARCEL_LOT', 'PARCEL_TRA', 'BlockDesig', 'FORM_PRIMA', 'FORM_ACCES', 'FORM_SIGNE', 'FORM_DATE', 'FORM_COMME', 'DEPARTMENT', 'PARCELTYPE', 'ZIPCODE', 'Shape_Leng', 'Shape_Area'], dtype='object')
foreclosures.describe()
ASSESSEDVALUE | YEAR | |
---|---|---|
count | 1.974000e+03 | 1982.000000 |
mean | 1.281877e+05 | 2011.934914 |
std | 8.889734e+04 | 2.875913 |
min | 0.000000e+00 | 2008.000000 |
25% | 8.446325e+04 | 2010.000000 |
50% | 1.136345e+05 | 2012.000000 |
75% | 1.453120e+05 | 2014.000000 |
max | 1.562129e+06 | 2019.000000 |
building_fires.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1258 entries, 652 to 2318 Data columns (total 10 columns): FID 1258 non-null int64 Join_Count 1258 non-null int64 TARGET_FID 1258 non-null int64 id 1258 non-null int64 id2 1258 non-null int64 type 1258 non-null object date 1258 non-null datetime64[ns] lat 1258 non-null float64 lon 1258 non-null float64 ADDRESS 1258 non-null object dtypes: datetime64[ns](1), float64(2), int64(5), object(2) memory usage: 108.1+ KB
# Visualizing the fire incidents in a map
fire_map = folium.Map(location=[43.54, -96.72], zoom_start=14, tiles='Stamen Terrain')
heat_df = building_fires[['lat', 'lon']]
heat_df = heat_df.dropna(axis=0, subset=['lat','lon'])
heat_data = [[row['lat'],row['lon']] for index, row in heat_df.iterrows()]
HeatMap(heat_data).add_to(fire_map)
fire_map
def cleanActivity(x):
if x == 11 or x == 12 or x == 13:
x = 'SINGLE OR TWO RESIDENTIAL'
elif x == 21 or x == 22 or x == 23 or x == 24 or x == 25:
x = 'MULTIFAMILY'
elif x in list(range(31,39)):
x = 'OFFICE AND PUBLIC SERVICE'
elif x in list(range(40,50)):
x = 'INSTITUTIONAL'
elif x in list(range(51, 54)):
x = 'COMMERCIAL'
elif x == 61 or x in list(range(63,70)):
x = 'INDUSTRIAL'
elif x == 62:
x = 'AIRPORT'
elif x in range(70,98) or x in range(0, 9):
x = 'OPEN SPACES/NA'
return x
parcels['ACTIVITY'] = parcels.ACTIVITY.apply(cleanActivity)
# Parcels dataset
df = pd.concat([parcels, pd.get_dummies(parcels.ACTIVITY, prefix='ACT_'), pd.get_dummies(parcels.PARPR), pd.get_dummies(parcels.PARCELTYPE), pd.get_dummies(parcels.PARTYPE), pd.get_dummies(parcels.COUNTY)], axis = 1)
df.PARHOUSE.fillna(method='bfill', inplace = True)
df = df[df.ACTIVITY != 'AIRPORT']
df = df[df.ACTIVITY != 'OPEN SPACES/NA']
parcels.drop(columns = ['BlockDesig', 'FORM_PRIMA', 'PARCELTYPE', 'FORM_COMME', 'PARCEL_TRA', 'PARCEL_LOT', 'created_us', 'TAG', 'ADDITION', 'ADDITIONPR', 'COUNTYID', 'OWNNAME1', 'OWNZIP', 'OWNZIP2', 'OWNNAME2', 'OWNCITY', 'OWNSTATE', 'OWNADDRESS', 'LEGAL', 'ADDITIONNU', 'PARHALF', 'PARPD', 'UNITNUM', 'GlobalID', 'last_edite', 'Shape_Leng', 'Shape_Area', 'DEPARTMENT', 'FORM_ACCES', 'FORM_SIGNE', 'FORM_DATE', 'created_da', 'last_edi_1', 'PARPR', 'PARTYPE', 'COUNTY', 'PARSTREET', 'LegalStart', 'ACTIVITY'], inplace = True)
df.drop(columns = ['BlockDesig', 'ACREAGE', 'FORM_PRIMA', 'PARCELTYPE', 'FORM_COMME', 'PARCEL_TRA', 'PARCEL_LOT', 'created_us', 'TAG', 'ADDITION', 'ADDITIONPR', 'COUNTYID', 'OWNNAME1', 'OWNZIP', 'OWNZIP2', 'OWNNAME2', 'OWNCITY', 'OWNSTATE', 'OWNADDRESS', 'LEGAL', 'ADDITIONNU', 'PARHALF', 'PARPD', 'UNITNUM', 'GlobalID', 'last_edite', 'Shape_Leng', 'Shape_Area', 'DEPARTMENT', 'FORM_ACCES', 'FORM_SIGNE', 'FORM_DATE', 'created_da', 'last_edi_1', 'PARPR', 'PARTYPE', 'COUNTY', 'PARSTREET', 'LegalStart', 'ACTIVITY'], inplace = True)
# Fire dataset
building_fires.drop(columns = ['FID', 'Join_Count', 'TARGET_FID', 'id', 'type', 'lat', 'lon', 'id2'], inplace = True)
building_fires = building_fires.groupby('ADDRESS').max()
building_fires['INCIDENT'] = 1
df = pd.merge(df, building_fires, how = 'left', on='ADDRESS')
df.INCIDENT.fillna(0, inplace = True)
df.date.fillna(pd.to_datetime('1/1/2008'), inplace = True)
# Foreclosure dataset
foreclosure_df = pd.DataFrame(foreclosures.groupby('ADDRESS').count())
foreclosure_df.rename(columns = { 'NAME' : 'FORECLOSED'}, inplace = True)
foreclosure_df.drop(columns = ['AUCTIONDATE', 'ASSESSEDVALUE', 'YEAR'], inplace = True)
df = pd.merge(df, foreclosure_df, how = 'left', on = 'ADDRESS')
df.FORECLOSED.fillna(0, inplace = True)
# Rent registry dataset
rent_reg.drop(columns = ['City_1', 'State_1', 'Permit_Num', 'Contact_Ty', 'Last_Name', 'First_Name', 'Middle_Ini', 'Business_N', 'Contact_Pr', 'Business_P', 'Mobile_Pho', 'Home_Phone', 'Email', 'Contact_Ad', 'Issue_Date'], inplace = True)
rent_reg.Units.fillna(value=rent_reg.Units.median(), inplace = True)
rent_reg_grouped = rent_reg.groupby('Address').mean()
rent_df = pd.DataFrame(rent_reg_grouped)
rent_df.rename(columns = { 'Address' : 'ADDRESS', 'Units' : 'RENT_REG_UNITS', 'YEAR' : 'RENT_REG_YEAR'}, inplace = True)
rent_df.rename_axis('ADDRESS', inplace = True)
rent_df['RENT_REG'] = 1
df = pd.merge(df, rent_df, how = 'left', on = 'ADDRESS')
df.RENT_REG.fillna(0, inplace = True)
df.RENT_REG_UNITS.fillna(0, inplace = True)
df.RENT_REG_YEAR.fillna(0, inplace = True)
# Utility disconnects dataset
utility_disconnects.rename(columns = {'Address' : 'ADDRESS', 'Year' : 'UTILITY_DISCONNECTS'}, inplace = True)
uti_disc_grouped = utility_disconnects.groupby('ADDRESS').count()
uti_disc_df = pd.DataFrame(uti_disc_grouped)
uti_disc_df['ANY_DISCONNECT'] = 1
df = pd.merge(df, uti_disc_df, how = 'left', on = 'ADDRESS')
df.UTILITY_DISCONNECTS.fillna(0, inplace = True)
df.ANY_DISCONNECT.fillna(0, inplace = True)
# Code cases
case_types = pd.get_dummies(code_cases.CaseType)
code_cases = pd.concat([code_cases, case_types], axis=1)
code_cases.drop(columns = ['Year'], inplace = True)
code_cases_grouped = code_cases.groupby(['ADDRESS']).sum()
code_cases_df = pd.DataFrame(code_cases_grouped)
code_cases_df['TOTAL_VIOLATIONS'] = code_cases_df.sum(axis=1)
code_cases_df['ANY_VIOLATIONS'] = 1
df = pd.merge(df, code_cases_df, how = 'left', on = 'ADDRESS')
df.ANY_VIOLATIONS.fillna(0, inplace = True)
df['Building Service'].fillna(0, inplace = True)
df['Drainage'].fillna(0, inplace = True)
df['Erosion and Sediment Control'].fillna(0, inplace = True)
df['Fire'].fillna(0, inplace = True)
df['Health Nuisance Complaints'].fillna(0, inplace = True)
df['IMPORT / Tree Survey & Stump Removal'].fillna(0, inplace = True)
df['Illicit Discharge'].fillna(0, inplace = True)
df['Landfill'].fillna(0, inplace = True)
df['Manufactured Housing'].fillna(0, inplace = True)
df['Parks and Rec - Dead or Diseased Tree'].fillna(0, inplace = True)
df['Parks and Rec - Tree Complaint'].fillna(0, inplace = True)
df['Property Maintenance'].fillna(0, inplace = True)
df['Rental Registration'].fillna(0, inplace = True)
df['Right of Way '].fillna(0, inplace = True)
df['Sidewalks and Ramps'].fillna(0, inplace = True)
df['Snow'].fillna(0, inplace = True)
df['Special Assessment'].fillna(0, inplace = True)
df['Vegetation'].fillna(0, inplace = True)
df['Waste Water'].fillna(0, inplace = True)
df['Water Purfication'].fillna(0, inplace = True)
df['Zoning'].fillna(0, inplace = True)
df.TOTAL_VIOLATIONS.fillna(0, inplace = True)
# Crime
crime.rename(columns = {'Offense' : 'CRIME_INCIDENT'}, inplace = True)
crime_grouped = crime.groupby('ADDRESS').count()
crime_df = pd.DataFrame(crime_grouped[['CRIME_INCIDENT']])
crime_df['ANY_CRIME'] = 1
df = pd.merge(df, crime_df, how = 'left', on = 'ADDRESS')
df.CRIME_INCIDENT.fillna(0, inplace = True)
df.ANY_CRIME.fillna(0, inplace = True)
# Tax assessment
minnehaha_tax.drop(columns=['TAG', 'COUNTYID', 'ACREAGE', 'SQFT', 'FRONTFOOT', 'PARHOUSE',
'PARHALF', 'PARPR', 'PARSTREET', 'PARTYPE', 'PARPD', 'LOT',
'BLOCK', 'TRACT', 'SUBDIVNO', 'ADDITION', 'LEGAL', 'COUNTY', 'MRTNSP',
'MRSCHD', 'SCHOOLDESC', 'MRZON1', 'NUMUNITS', 'MRLYAP', 'MAP_ID'], inplace = True)
lincoln_tax['ADDRESS'] = lincoln_tax.Address
lincoln_tax.drop(columns = ['FID', 'OBJECTID', 'PID', 'Plat', 'SchoolDist', 'Township', 'STR',
'Address', 'Name', 'Add1', 'Add2', 'Add3', 'Zip', 'Legal1', 'Legal2',
'Legal3', 'Legal4', 'Class1_1', 'Class2_1','Class3_1', 'Class4_1', 'Class5_1'], inplace = True)
combined_tax = pd.merge(lincoln_tax, minnehaha_tax, how = 'outer', on = 'ADDRESS')
combined_tax.fillna(0, inplace = True)
combined_tax['LANDVALUE'] = combined_tax.MRLNVC + combined_tax.Value1_1
combined_tax['BUILDVALUE'] = combined_tax.MRBDVC + combined_tax.Value12_1
combined_tax['TOTALVALUE'] = combined_tax[['MRTOTC' , 'Value1_1', 'Value12_1', 'Value13_1', 'Value14_1', 'Value15_1']].sum(axis=1)
combined_tax.drop(columns=['Value1_1', 'Value12_1', 'Value13_1','Value14_1', 'Value15_1', 'MRLNVC', 'MRBDVC', 'MRTOTC'], inplace = True)
df = pd.merge(df, combined_tax, how = 'left', on = 'ADDRESS')
df = df[df.ADDRESS != '0']
df = df.dropna(subset=['ADDRESS'])
df.LANDVALUE.fillna(0, inplace = True)
df.BUILDVALUE.fillna(0, inplace = True)
df.TOTALVALUE.fillna(0, inplace = True)
df.set_index('ADDRESS', inplace = True)
# Moving the incident column to the last position
df1 = df.pop('INCIDENT')
df['INCIDENT'] = df1
# Calculate all addresses with at least one fire incident
df.INCIDENT.value_counts()
0.0 54127 1.0 1121 Name: INCIDENT, dtype: int64
## Hide the 2018 data
# First create a new column for parcels with incidents in 2018
df['INCIDENT_2018'] = [1 if date > pd.to_datetime('2018') else 0 for date in df['date']]
# Now turn all 2018 fire incidents off
df.loc[(df.date > '2018') & (df.INCIDENT == 1), 'INCIDENT'] = 0
# Calculate all addresses with at least one fire incident, excluding 2018 fires
df.INCIDENT.value_counts()
0.0 54221 1.0 1027 Name: INCIDENT, dtype: int64
# Split predictor and prediction variables
X = df.drop(columns = ['INCIDENT', 'date'])
y = df.INCIDENT
# Optimizing the algorithm for the best possible results
# Create pipeline with feature selector and random forest classifier
pipe = Pipeline([
('feature_selection', SelectKBest(f_classif)),
('clf', RandomForestClassifier(random_state=2))])
# Create a parameter grid to test
params = {
'feature_selection__k':[3, 5, 10, 20, 50],
'clf__n_estimators':[2, 5, 10, 100],
'clf__max_depth' : [3, 5, 10, 20, 50]}
# Initialize the grid search object
grid_search = GridSearchCV(pipe, param_grid=params)
# Fit it to the data and print the best value combination
print(grid_search.fit(X, y).best_params_)
C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 33] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13 35] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 6 7 13 28 38 39 51 65] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw
{'clf__max_depth': 10, 'clf__n_estimators': 100, 'feature_selection__k': 20}
# Select the best features based on the optimized parameters
sk = SelectKBest(f_classif, k=grid_search.best_params_['feature_selection__k'])
which_selected = sk.fit(X, y).get_support()
X = X[X.columns[which_selected]]
# Fit the classifier with the optimized parameters
random_forest = RandomForestClassifier(n_estimators=grid_search.best_params_['clf__n_estimators'], max_depth = grid_search.best_params_['clf__max_depth'])
random_forest.fit(X, y)
C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:113: UserWarning: Features [ 7 13] are constant. UserWarning) C:\Users\User\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py:114: RuntimeWarning: invalid value encountered in true_divide f = msb / msw
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=10, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)
# Correlation heat map with our predictive variables
comb = pd.concat([X, y], axis = 1)
corr = comb.corr()
plt.figure(figsize = (10,10))
dropSelf = np.zeros_like(corr)
dropSelf[np.triu_indices_from(dropSelf)] = True
ax = sns.heatmap(
corr,
vmin=-1, vmax=1, center=0,
cmap=sns.diverging_palette(20, 220, n=200),
square=True, annot=True, annot_kws={"size": 7},
mask=dropSelf
)
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
)
plt.savefig('Results - Correlation matrix.pdf', bbox_inches = 'tight', pad_inches = 2.5)
# Cross validation score for Random Forest
Score = cross_val_score(random_forest, X, y, cv=3)
round(np.mean(Score)*100, ndigits=4)
98.1502
# Assign a probability score to each address
df['PREDICTION'] = random_forest.predict_proba(X)[:,1]
df.PREDICTION.describe()
count 55248.000000 mean 0.018395 std 0.057942 min 0.001134 25% 0.005648 50% 0.006841 75% 0.012729 max 0.989337 Name: PREDICTION, dtype: float64
# Compare fires found in 2018 in the 2000 highest risk properties versus randomness
np.random.seed(0)
df['RANDOM'] = np.random.rand(df.shape[0])
df_random = df.nlargest(2000, columns = 'RANDOM')
rf_results_search = df.nlargest(2000, columns = 'PREDICTION')
print('Total 2018 predicted fires (2000 interventions): ' + str(rf_results_search.INCIDENT_2018.sum()))
print('Total random predicted fires (2000 interventions): ' + str(df_random.INCIDENT_2018.sum()))
Total 2018 predicted fires (2000 interventions): 42 Total random predicted fires (2000 interventions): 6
# Visualize how fire risk is distributed among parcel addresses
df.PREDICTION.plot.hist()
<matplotlib.axes._subplots.AxesSubplot at 0x24e58125ef0>
# Plotting the comparison between randomness and sorting by highest risk with our algorithm
random_results = df_random.INCIDENT_2018.sum()
rf_results = rf_results_search.INCIDENT_2018.sum()
df_results = pd.DataFrame({'Model' : ['Random search', 'RF - Highest risk'], 'results' : [random_results, rf_results]})
ax = df_results.plot.bar(x = 'Model', y = 'results', color = ['orange', 'b'])
plt.title('Results based on 2000 addresses in a year')
plt.ylabel('Fires catched')
ax.set_xticklabels(
ax.get_xticklabels(),
rotation=45,
horizontalalignment='right'
)
plt.savefig('Results - Model comparison.pdf', bbox_inches = 'tight', pad_inches = 0.5)
# Comparing our predictive model to randomness
model_comparison = []
for searches in range(0, 55000, 100):
prediction = df.nlargest(searches, columns = 'PREDICTION').INCIDENT_2018.sum()
random = df.nlargest(searches, columns = 'RANDOM').INCIDENT_2018.sum()
model_comparison.append([prediction, random])
model_comparison_df = pd.DataFrame(model_comparison, columns=['PREDICTION', 'RANDOM'])
model_comparison_df.plot()
plt.xlabel('Properties inspected or intervened')
plt.ylabel('Fire occurance in properties inspected')
plt.xticks([])
plt.savefig('Model comparison - properties intervened.pdf', bbox_inches = 'tight', pad_inches = 2.5)
# ROC graph
y_test = df.INCIDENT_2018
preds = df.PREDICTION
fpr, tpr, threshold = roc_curve(y_test, preds)
roc_auc = auc(fpr, tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.savefig('AUC ROC curve.pdf', bbox_inches = 'tight', pad_inches = 2.5)
# Feature importance from the RFC
feature_importances = pd.DataFrame(random_forest.feature_importances_,
index = X.columns,
columns=['importance']).sort_values('importance',
ascending=False)
feature_importances
importance | |
---|---|
CRIME_INCIDENT | 0.202975 |
SQFT | 0.185457 |
FRONTFOOT | 0.095755 |
NUMUNITS | 0.072335 |
TOTALVALUE | 0.066862 |
BUILDVALUE | 0.063689 |
LANDVALUE | 0.062413 |
LANDUSE | 0.056735 |
RENT_REG_UNITS | 0.037084 |
RENT_REG_YEAR | 0.026956 |
ANY_CRIME | 0.021229 |
TOTAL_VIOLATIONS | 0.020655 |
Condo or Suite | 0.015525 |
Property Maintenance | 0.015104 |
Standard Tax Parcel | 0.014677 |
ACT__MULTIFAMILY | 0.014435 |
ACT__SINGLE OR TWO RESIDENTIAL | 0.009917 |
ANY_VIOLATIONS | 0.007761 |
RENT_REG | 0.007503 |
ACT__COMMERCIAL | 0.002931 |
# Visualizing the feature importances through a graph
importances = random_forest.feature_importances_
indices = np.argsort(importances)
ax = plt.barh(range(len(indices)), importances[indices], color='b', align='center')
plt.yticks(range(len(indices)), [X.columns[i] for i in indices])
plt.xlabel('Relative Importance')
plt.savefig('Results - Feature importance.pdf', bbox_inches = 'tight', pad_inches = 0.5)
# Export the results to CSV
df['PREDICTION'].to_csv('Results - risk predictions.csv', header = True)