Md. Abrar Jahin
Andrii Krutsylo
After importing libraries and packages, we start off by defining a function transform_to_supervised
that creates desired lag (24 hours in this case) and forecasting features (1 hour) of our independent variables concatening with the dataframe and returns the final dataframe.
import os
import optuna
import pickle
import pandas as pd
from optuna import Trial
from optuna.samplers import TPESampler
from sklearn.impute import KNNImputer
from sklearn.model_selection import StratifiedKFold, cross_val_score
from xgboost import XGBClassifier, XGBRegressor
from matplotlib import pyplot as plt
from sklearn.metrics import mean_absolute_error, accuracy_score, balanced_accuracy_score
import numpy as np
def transform_to_supervised(df,
previous_steps=1,
forecast_steps=1,
dropnan=False):
"""
https://gist.github.com/monocongo/6e0df19c9dd845f3f465a9a6ccfcef37
Transforms a DataFrame containing time series data into a DataFrame
containing data suitable for use as a supervised learning problem.
Derived from code originally found at
https://machinelearningmastery.com/convert-time-series-supervised-learning-problem-python/
:param df: pandas DataFrame object containing columns of time series values
:param previous_steps: the number of previous steps that will be included in the
output DataFrame corresponding to each input column
:param forecast_steps: the number of forecast steps that will be included in the
output DataFrame corresponding to each input column
:return Pandas DataFrame containing original columns, renamed <orig_name>(t), as well as
columns for previous steps, <orig_name>(t-1) ... <orig_name>(t-n) and columns
for forecast steps, <orig_name>(t+1) ... <orig_name>(t+n)
"""
# original column names
col_names = df.columns
# list of columns and corresponding names we'll build from
# the originals found in the input DataFrame
cols, names = list(), list()
# input sequence (t-n, ... t-1)
# Lag features
for i in range(previous_steps, 0, -1):
cols.append(df.shift(i))
names += [('%s(t-%d)' % (col_name, i)) for col_name in col_names]
# forecast sequence (t, t+1, ... t+n)
for i in range(0, forecast_steps):
cols.append(df.shift(-i))
if i == 0:
names += [('%s(t)' % col_name) for col_name in col_names]
else:
names += [('%s(t+%d)' % (col_name, i)) for col_name in col_names]
# put all the columns together into a single aggregated DataFrame
agg = pd.concat(cols, axis=1)
agg.columns = names
# drop rows with NaN values
if dropnan:
agg.dropna(inplace=True)
return agg
The dataset has been collected from the Smart Road - Winter Road Maintenance Challenge 2021 organized by UiT The Arctic University of Norway on Devpost.
Dataset download link: https://uitno.app.box.com/s/bch09z27weq0wpcv8dbbc18sxz6cycjt
After downloading the smart_road_measurements.csv
file from the competition page, wehad added extra columns collecting data from the external resources authorized the organizers. The links of the external datasets are:
[1] Weather data https://pypi.org/project/wwo-hist/
[2] UV Index data https://pyowm.readthedocs.io/en/latest/v3/uv-api-usage-examples.html
After merging these 3 files together based on the same dates, we finalized our main dataset smart_road_measurements_new_d_weather.csv
on top of which we will build our model after preprocessing.
df = pd.read_csv("/content/smart_road_measurements_new_d_weather.csv", header=0)
df2 = df.copy()
df.head(15)
Date | Time(+01:00) | Friction | State | Ta | Tsurf | Water | Height | Distance | maxtempC | mintempC | totalSnow_cm | sunHour | uvIndex | moon_illumination | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | UV radiation | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | 07:11:19 | 0.33 | 6 | -2.4 | -6.07 | 0.0 | 118.0 | 0 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
1 | 2021.02.16 | 07:11:20 | 0.33 | 6 | -2.4 | -6.12 | 0.0 | 118.0 | 5 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
2 | 2021.02.16 | 07:11:21 | 0.33 | 6 | -2.4 | -6.17 | 0.0 | 118.0 | 5 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
3 | 2021.02.16 | 07:11:22 | 0.34 | 6 | -2.4 | -6.17 | 0.0 | 118.0 | 10 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
4 | 2021.02.16 | 07:11:23 | 0.33 | 6 | -2.4 | -6.17 | 0.0 | 118.0 | 14 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
5 | 2021.02.16 | 07:11:24 | 0.34 | 6 | -2.4 | -6.67 | 0.0 | 118.0 | 18 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
6 | 2021.02.16 | 07:11:25 | 0.34 | 6 | -2.4 | -6.77 | 0.0 | 118.0 | 22 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
7 | 2021.02.16 | 07:11:26 | 0.35 | 6 | -2.4 | -6.83 | 0.0 | 118.0 | 26 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
8 | 2021.02.16 | 07:11:27 | 0.35 | 6 | -2.4 | -6.97 | 0.0 | 117.0 | 30 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
9 | 2021.02.16 | 07:11:28 | 0.35 | 6 | -2.4 | -6.99 | 0.0 | 116.0 | 34 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
10 | 2021.02.16 | 07:11:29 | 0.36 | 6 | -2.4 | -7.03 | 0.0 | 116.0 | 40 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
11 | 2021.02.16 | 07:11:30 | 0.37 | 6 | -2.4 | -7.08 | 0.0 | 116.0 | 40 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
12 | 2021.02.16 | 07:11:31 | 0.39 | 6 | -2.4 | -7.15 | 0.0 | 117.0 | 40 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
13 | 2021.02.16 | 07:11:32 | 0.40 | 6 | -2.4 | -7.02 | 0.0 | 117.0 | 40 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
14 | 2021.02.16 | 07:11:33 | 0.41 | 6 | -2.4 | -7.01 | 0.0 | 117.0 | 40 | -6 | -11 | 0.0 | 8.0 | 2 | 27 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
Our dataset contains 349613 rows and 29 columns
df.shape
(349613, 29)
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 349613 entries, 0 to 349612 Data columns (total 29 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 349613 non-null object 1 Time(+01:00) 349613 non-null object 2 Friction 349613 non-null float64 3 State 349613 non-null int64 4 Ta 349613 non-null float64 5 Tsurf 349613 non-null float64 6 Water 349613 non-null float64 7 Height 347863 non-null float64 8 Distance 349613 non-null int64 9 maxtempC 349613 non-null int64 10 mintempC 349613 non-null int64 11 totalSnow_cm 349613 non-null float64 12 sunHour 349613 non-null float64 13 uvIndex 349613 non-null int64 14 moon_illumination 349613 non-null int64 15 DewPointC 349613 non-null int64 16 FeelsLikeC 349613 non-null int64 17 HeatIndexC 349613 non-null int64 18 WindChillC 349613 non-null int64 19 WindGustKmph 349613 non-null int64 20 cloudcover 349613 non-null int64 21 humidity 349613 non-null int64 22 precipMM 349613 non-null float64 23 pressure 349613 non-null int64 24 tempC 349613 non-null int64 25 visibility 349613 non-null int64 26 winddirDegree 349613 non-null int64 27 windspeedKmph 349613 non-null int64 28 UV radiation 349613 non-null float64 dtypes: float64(9), int64(18), object(2) memory usage: 77.4+ MB
import numpy as np
np.random.seed(0)
import seaborn as sns
sns.set_theme()
_ = sns.heatmap(df2.iloc[:,2:11].corr())
_ = sns.heatmap(df2.corr())
We want to predict Friction of the road by weather conditions. So, this is a classification task. Every day the car drives on a new route. This means that all 11 days we receive data on new road sections. So, the only link between the road sections is the average weather conditions.
This can be achieved by filtering the rows on Microsoft Excel for each date and get the total distance covered (the last row on each date because the column is cumulative in nature)
Max Distance traveled, Date
42441, 16/02/2021
92311, 17/02/2021
150216, 18/02/2021
39007, 19/02/2021
71358, 22/02/2021
81999, 23/02/2021
55958, 24/02/2021
77315, 25/02/2021
55647, 26/02/2021
61534, 1/03/2021
12409, 2/03/2021
Therefore, we can see from the above data that for all 11 days the car was driving at different routes
We drop the Distance
because the condition of the road does not depend on how much the car has traveled before. We use this column to get the speed and slope of the road.
This means that we are using normalized data + lag (time-series
classification with engineered features instead of time-series classification with deep learning, because we have shallow data). We won't focus on any complicated models, just XGBClassifier to win.
requires caution (label-1) and safe (label-2).
Ta, Tsurf, friction are highly correlated which has been shown in our pandas profiling https://krutsylo.neocities.org/SmartRoads/pandas3.html of the smart road dataset.
Yet we'll drop State, Height, Distance, Ta, Tsurf, Water, moon-illumination, uvIndex columns
df = df.drop("Height", axis=1) # contain N/A
df = df.drop("Distance", axis=1)
df = df.drop("State", axis=1)
df = df.drop("Ta", axis=1)
df = df.drop("Tsurf", axis=1)
df = df.drop("Water", axis=1)
df = df.drop("moon_illumination", axis=1)
df = df.drop("uvIndex", axis=1)
df.head()
Date | Time(+01:00) | Friction | maxtempC | mintempC | totalSnow_cm | sunHour | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | UV radiation | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | 07:11:19 | 0.33 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
1 | 2021.02.16 | 07:11:20 | 0.33 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
2 | 2021.02.16 | 07:11:21 | 0.33 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
3 | 2021.02.16 | 07:11:22 | 0.34 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
4 | 2021.02.16 | 07:11:23 | 0.33 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
We have grouped the data by calculating the mean of the rows in each hour based on the individual dates. For instance, if there are 8 rows for each hour, we calculated the mean of 8 rows and thus converted into a single row belonging to the distinct dates.
We also avoided duplicates to reduce the noise in the data.
df['Time(+01:00)'] = pd.to_datetime(df['Time(+01:00)'], format='%H:%M:%S').dt.hour
df = df.groupby(['Date','Time(+01:00)']).mean()
df = df.drop_duplicates()
df.head()
Friction | maxtempC | mintempC | totalSnow_cm | sunHour | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | UV radiation | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | Time(+01:00) | |||||||||||||||||||
2021.02.16 | 7 | 0.359750 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
8 | 0.379456 | -6 | -11 | 0.0 | 8.0 | -14 | -15 | -9 | -15 | 26 | 0 | 66 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 | |
9 | 0.374158 | -6 | -11 | 0.0 | 8.0 | -13 | -14 | -8 | -14 | 25 | 0 | 67 | 0.0 | 1020 | -8 | 10 | 113 | 14 | 0.2 | |
10 | 0.377506 | -6 | -11 | 0.0 | 8.0 | -12 | -13 | -7 | -13 | 24 | 7 | 68 | 0.0 | 1020 | -7 | 10 | 112 | 14 | 0.2 | |
11 | 0.450421 | -6 | -11 | 0.0 | 8.0 | -11 | -12 | -6 | -12 | 22 | 14 | 69 | 0.0 | 1020 | -6 | 10 | 112 | 14 | 0.2 |
Now we will work on the target feature that is Friction
column to accomplish our objective since we want to perform a supervised machine learning model. Here we applied our knowledge of physics and research capabilities.
Icy: These roads typically have the lowest coefficient of friction. For drivers, this is the most dangerous surface to be on. The small coefficient of friction gives the driver the least amount of traction when accelerating, braking, or turning (which has angular acceleration). Icy roads have a frictional coefficient of around 0.1.
Wet: Roads wet with water have a coefficient of friction of around .4. This is around 4 times higher than an icy road. Although these roads are much safer to drive on, there is still the possibility of hydroplaning. Hydroplaning occurs when there is standing or flowing water on the road (typically from rainfall) that causes a tire to lose contact with the road's surface. The treads are designed to allow water to fill the crevices so that contact may be maintained between the road and the tire. However, if there is too much water, this may not be achieved, and hydroplaning will occur. This is precisely the reason that racing slicks have such a high coefficient of friction on dry roads (about .9) and a much lower coefficient on wet roads (as low as .1).
Dry: Roads without precipitation are considered optimal for driving conditions. They have the highest coefficient of friction, around 0.9, which creates the most traction. This allows corners, acceleration, and braking to reach higher values without loss of control. Oftentimes, if roads are not dry, races will be canceled due to the extreme dangers that a less than optimal frictional surface can pose.
So, we'll take (0 <= friction < 0.5) as dangerous, and (0.5 < friction <= 1) as safe
bins = [0, 0.5, 1]
labels = [0, 1]
df["Friction"] = pd.cut(df["Friction"], bins, labels=labels)
#df = df.drop("Date", axis=1)
#df = df.drop("Time(+01:00)", axis=1)
df.head()
Friction | maxtempC | mintempC | totalSnow_cm | sunHour | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | UV radiation | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | Time(+01:00) | |||||||||||||||||||
2021.02.16 | 7 | 0 | -6 | -11 | 0.0 | 8.0 | -15 | -16 | -9 | -16 | 27 | 0 | 65 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 |
8 | 0 | -6 | -11 | 0.0 | 8.0 | -14 | -15 | -9 | -15 | 26 | 0 | 66 | 0.0 | 1020 | -9 | 10 | 113 | 14 | 0.2 | |
9 | 0 | -6 | -11 | 0.0 | 8.0 | -13 | -14 | -8 | -14 | 25 | 0 | 67 | 0.0 | 1020 | -8 | 10 | 113 | 14 | 0.2 | |
10 | 0 | -6 | -11 | 0.0 | 8.0 | -12 | -13 | -7 | -13 | 24 | 7 | 68 | 0.0 | 1020 | -7 | 10 | 112 | 14 | 0.2 | |
11 | 0 | -6 | -11 | 0.0 | 8.0 | -11 | -12 | -6 | -12 | 22 | 14 | 69 | 0.0 | 1020 | -6 | 10 | 112 | 14 | 0.2 |
Now we'll perform lagging and forecasting feature columns by shifting simply using our pre-defined transform_to_supervise
function.
df = transform_to_supervised(df, previous_steps=24, forecast_steps=1, dropnan=True)
Y = df.loc[:, "Friction(t)"].to_numpy()
cols = [c for c in df.columns if '(t)' not in c]
data=df[cols]
data['Friction'] = Y
data.to_csv('/content/test.csv')
data = data.values.tolist()
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df[cols].head()
Friction(t-24) | maxtempC(t-24) | mintempC(t-24) | totalSnow_cm(t-24) | sunHour(t-24) | DewPointC(t-24) | FeelsLikeC(t-24) | HeatIndexC(t-24) | WindChillC(t-24) | WindGustKmph(t-24) | cloudcover(t-24) | humidity(t-24) | precipMM(t-24) | pressure(t-24) | tempC(t-24) | visibility(t-24) | winddirDegree(t-24) | windspeedKmph(t-24) | UV radiation(t-24) | Friction(t-23) | maxtempC(t-23) | mintempC(t-23) | totalSnow_cm(t-23) | sunHour(t-23) | DewPointC(t-23) | FeelsLikeC(t-23) | HeatIndexC(t-23) | WindChillC(t-23) | WindGustKmph(t-23) | cloudcover(t-23) | humidity(t-23) | precipMM(t-23) | pressure(t-23) | tempC(t-23) | visibility(t-23) | winddirDegree(t-23) | windspeedKmph(t-23) | UV radiation(t-23) | Friction(t-22) | maxtempC(t-22) | ... | windspeedKmph(t-3) | UV radiation(t-3) | Friction(t-2) | maxtempC(t-2) | mintempC(t-2) | totalSnow_cm(t-2) | sunHour(t-2) | DewPointC(t-2) | FeelsLikeC(t-2) | HeatIndexC(t-2) | WindChillC(t-2) | WindGustKmph(t-2) | cloudcover(t-2) | humidity(t-2) | precipMM(t-2) | pressure(t-2) | tempC(t-2) | visibility(t-2) | winddirDegree(t-2) | windspeedKmph(t-2) | UV radiation(t-2) | Friction(t-1) | maxtempC(t-1) | mintempC(t-1) | totalSnow_cm(t-1) | sunHour(t-1) | DewPointC(t-1) | FeelsLikeC(t-1) | HeatIndexC(t-1) | WindChillC(t-1) | WindGustKmph(t-1) | cloudcover(t-1) | humidity(t-1) | precipMM(t-1) | pressure(t-1) | tempC(t-1) | visibility(t-1) | winddirDegree(t-1) | windspeedKmph(t-1) | UV radiation(t-1) | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Date | Time(+01:00) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
2021.02.18 | 11 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -15.0 | -16.0 | -9.0 | -16.0 | 27.0 | 0.0 | 65.0 | 0.0 | 1020.0 | -9.0 | 10.0 | 113.0 | 14.0 | 0.2 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -14.0 | -15.0 | -9.0 | -15.0 | 26.0 | 0.0 | 66.0 | 0.0 | 1020.0 | -9.0 | 10.0 | 113.0 | 14.0 | 0.2 | 0 | -6.0 | ... | 8.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -18.0 | -12.0 | -7.0 | -12.0 | 17.0 | 7.0 | 42.0 | 0.0 | 1012.0 | -7.0 | 10.0 | 104.0 | 8.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -17.0 | -12.0 | -8.0 | -12.0 | 16.0 | 8.0 | 47.0 | 0.0 | 1012.0 | -8.0 | 10.0 | 101.0 | 8.0 | 0.21 |
12 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -14.0 | -15.0 | -9.0 | -15.0 | 26.0 | 0.0 | 66.0 | 0.0 | 1020.0 | -9.0 | 10.0 | 113.0 | 14.0 | 0.2 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -13.0 | -14.0 | -8.0 | -14.0 | 25.0 | 0.0 | 67.0 | 0.0 | 1020.0 | -8.0 | 10.0 | 113.0 | 14.0 | 0.2 | 0 | -6.0 | ... | 8.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -17.0 | -12.0 | -8.0 | -12.0 | 16.0 | 8.0 | 47.0 | 0.0 | 1012.0 | -8.0 | 10.0 | 101.0 | 8.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -12.0 | -8.0 | -12.0 | 15.0 | 9.0 | 51.0 | 0.0 | 1012.0 | -8.0 | 10.0 | 97.0 | 7.0 | 0.21 | |
13 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -13.0 | -14.0 | -8.0 | -14.0 | 25.0 | 0.0 | 67.0 | 0.0 | 1020.0 | -8.0 | 10.0 | 113.0 | 14.0 | 0.2 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -12.0 | -13.0 | -7.0 | -13.0 | 24.0 | 7.0 | 68.0 | 0.0 | 1020.0 | -7.0 | 10.0 | 112.0 | 14.0 | 0.2 | 0 | -6.0 | ... | 8.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -12.0 | -8.0 | -12.0 | 15.0 | 9.0 | 51.0 | 0.0 | 1012.0 | -8.0 | 10.0 | 97.0 | 7.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -12.0 | -8.0 | -12.0 | 14.0 | 10.0 | 55.0 | 0.0 | 1012.0 | -8.0 | 10.0 | 94.0 | 7.0 | 0.21 | |
14 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -12.0 | -13.0 | -7.0 | -13.0 | 24.0 | 7.0 | 68.0 | 0.0 | 1020.0 | -7.0 | 10.0 | 112.0 | 14.0 | 0.2 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -11.0 | -12.0 | -6.0 | -12.0 | 22.0 | 14.0 | 69.0 | 0.0 | 1020.0 | -6.0 | 10.0 | 112.0 | 14.0 | 0.2 | 0 | -6.0 | ... | 7.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -12.0 | -8.0 | -12.0 | 14.0 | 10.0 | 55.0 | 0.0 | 1012.0 | -8.0 | 10.0 | 94.0 | 7.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -13.0 | -9.0 | -13.0 | 15.0 | 11.0 | 57.0 | 0.0 | 1012.0 | -9.0 | 10.0 | 94.0 | 7.0 | 0.21 | |
15 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -11.0 | -12.0 | -6.0 | -12.0 | 22.0 | 14.0 | 69.0 | 0.0 | 1020.0 | -6.0 | 10.0 | 112.0 | 14.0 | 0.2 | 0 | -6.0 | -11.0 | 0.0 | 8.0 | -10.0 | -11.0 | -6.0 | -11.0 | 21.0 | 22.0 | 69.0 | 0.0 | 1020.0 | -6.0 | 10.0 | 111.0 | 13.0 | 0.2 | 0 | -6.0 | ... | 7.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -13.0 | -9.0 | -13.0 | 15.0 | 11.0 | 57.0 | 0.0 | 1012.0 | -9.0 | 10.0 | 94.0 | 7.0 | 0.21 | 0 | -7.0 | -18.0 | 0.0 | 8.5 | -16.0 | -14.0 | -10.0 | -14.0 | 16.0 | 12.0 | 60.0 | 0.0 | 1012.0 | -10.0 | 10.0 | 93.0 | 8.0 | 0.21 |
5 rows × 456 columns
Lag of 1 to 3 days
lag = pd.read_csv('/content/lag(1-3)days.csv')
lag=lag.head(10)
lag
Date | DewPointC(t-3) | DewPointC(t-2) | DewPointC(t-1) | DewPointC | FeelsLikeC(t-3) | FeelsLikeC(t-2) | FeelsLikeC(t-1) | FeelsLikeC | HeatIndexC(t-3) | HeatIndexC(t-2) | HeatIndexC(t-1) | HeatIndexC | WindChillC(t-3) | WindChillC(t-2) | WindChillC(t-1) | WindChillC | WindGustKmph(t-3) | WindGustKmph(t-2) | WindGustKmph(t-1) | WindGustKmph | cloudcover(t-3) | cloudcover(t-2) | cloudcover(t-1) | cloudcover | humidity(t-3) | humidity(t-2) | humidity(t-1) | humidity | precipMM(t-3) | precipMM(t-2) | precipMM(t-1) | precipMM | pressure(t-3) | pressure(t-2) | pressure(t-1) | pressure | tempC(t-3) | tempC(t-2) | tempC(t-1) | tempC | visibility(t-3) | visibility(t-2) | visibility(t-1) | visibility | winddirDegree(t-3) | winddirDegree(t-2) | winddirDegree(t-1) | winddirDegree | windspeedKmph(t-3) | windspeedKmph(t-2) | windspeedKmph(t-1) | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | NaN | NaN | -6.875000 | -12.625000 | NaN | NaN | -8.666667 | -13.166667 | NaN | NaN | -4.958333 | -7.833333 | NaN | NaN | -8.666667 | -13.166667 | NaN | NaN | 13.125000 | 24.458333 | NaN | NaN | 77.875000 | 5.458333 | NaN | NaN | 87.000000 | 66.625000 | NaN | NaN | 0.316667 | 0.000000 | NaN | NaN | 1024.500000 | 1019.583333 | NaN | NaN | -5.083333 | -8.000000 | NaN | NaN | 5.916667 | 10.000000 | NaN | NaN | 159.291667 | 113.250000 | NaN | NaN | 8.333333 | 13.041667 |
1 | 2021.02.17 | NaN | -6.875000 | -12.625000 | -15.291667 | NaN | -8.666667 | -13.166667 | -8.875000 | NaN | -4.958333 | -7.833333 | -5.708333 | NaN | -8.666667 | -13.166667 | -8.875000 | NaN | 13.125000 | 24.458333 | 16.583333 | NaN | 77.875000 | 5.458333 | 6.666667 | NaN | 87.000000 | 66.625000 | 46.291667 | NaN | 0.316667 | 0.000000 | 0.000000 | NaN | 1024.500000 | 1019.583333 | 1015.875000 | NaN | -5.083333 | -8.000000 | -5.833333 | NaN | 5.916667 | 10.000000 | 10.000000 | NaN | 159.291667 | 113.250000 | 129.625000 | NaN | 8.333333 | 13.041667 | 7.916667 |
2 | 2021.02.18 | -6.875000 | -12.625000 | -15.291667 | -17.750000 | -8.666667 | -13.166667 | -8.875000 | -13.166667 | -4.958333 | -7.833333 | -5.708333 | -9.375000 | -8.666667 | -13.166667 | -8.875000 | -13.166667 | 13.125000 | 24.458333 | 16.583333 | 17.791667 | 77.875000 | 5.458333 | 6.666667 | 21.666667 | 87.000000 | 66.625000 | 46.291667 | 49.875000 | 0.316667 | 0.000000 | 0.000000 | 0.000000 | 1024.500000 | 1019.583333 | 1015.875000 | 1012.458333 | -5.083333 | -8.000000 | -5.833333 | -10.083333 | 5.916667 | 10.000000 | 10.000000 | 10.000000 | 159.291667 | 113.250000 | 129.625000 | 99.833333 | 8.333333 | 13.041667 | 7.916667 | 8.666667 |
3 | 2021.02.19 | -12.625000 | -15.291667 | -17.750000 | -17.166667 | -13.166667 | -8.875000 | -13.166667 | -22.666667 | -7.833333 | -5.708333 | -9.375000 | -15.041667 | -13.166667 | -8.875000 | -13.166667 | -22.666667 | 24.458333 | 16.583333 | 17.791667 | 34.250000 | 5.458333 | 6.666667 | 21.666667 | 15.208333 | 66.625000 | 46.291667 | 49.875000 | 78.458333 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1019.583333 | 1015.875000 | 1012.458333 | 1013.083333 | -8.000000 | -5.833333 | -10.083333 | -15.250000 | 10.000000 | 10.000000 | 10.000000 | 10.000000 | 113.250000 | 129.625000 | 99.833333 | 103.416667 | 13.041667 | 7.916667 | 8.666667 | 17.041667 |
4 | 2021.02.22 | -15.291667 | -17.750000 | -17.166667 | -6.250000 | -8.875000 | -13.166667 | -22.666667 | -9.875000 | -5.708333 | -9.375000 | -15.041667 | -4.791667 | -8.875000 | -13.166667 | -22.666667 | -9.875000 | 16.583333 | 17.791667 | 34.250000 | 25.000000 | 6.666667 | 21.666667 | 15.208333 | 83.333333 | 46.291667 | 49.875000 | 78.458333 | 88.041667 | 0.000000 | 0.000000 | 0.000000 | 0.295833 | 1015.875000 | 1012.458333 | 1013.083333 | 1016.500000 | -5.833333 | -10.083333 | -15.250000 | -4.875000 | 10.000000 | 10.000000 | 10.000000 | 5.333333 | 129.625000 | 99.833333 | 103.416667 | 126.333333 | 7.916667 | 8.666667 | 17.041667 | 13.041667 |
5 | 2021.02.23 | -17.750000 | -17.166667 | -6.250000 | -4.208333 | -13.166667 | -22.666667 | -9.875000 | -6.291667 | -9.375000 | -15.041667 | -4.791667 | -2.875000 | -13.166667 | -22.666667 | -9.875000 | -6.291667 | 17.791667 | 34.250000 | 25.000000 | 16.708333 | 21.666667 | 15.208333 | 83.333333 | 86.166667 | 49.875000 | 78.458333 | 88.041667 | 87.125000 | 0.000000 | 0.000000 | 0.295833 | 0.037500 | 1012.458333 | 1013.083333 | 1016.500000 | 1014.375000 | -10.083333 | -15.250000 | -4.875000 | -3.208333 | 10.000000 | 10.000000 | 5.333333 | 8.708333 | 99.833333 | 103.416667 | 126.333333 | 150.083333 | 8.666667 | 17.041667 | 13.041667 | 8.666667 |
6 | 2021.02.24 | -17.166667 | -6.250000 | -4.208333 | -2.208333 | -22.666667 | -9.875000 | -6.291667 | -5.250000 | -15.041667 | -4.791667 | -2.875000 | -1.541667 | -22.666667 | -9.875000 | -6.291667 | -5.250000 | 34.250000 | 25.000000 | 16.708333 | 22.208333 | 15.208333 | 83.333333 | 86.166667 | 93.541667 | 78.458333 | 88.041667 | 87.125000 | 87.708333 | 0.000000 | 0.295833 | 0.037500 | 0.820833 | 1013.083333 | 1016.500000 | 1014.375000 | 1000.750000 | -15.250000 | -4.875000 | -3.208333 | -2.166667 | 10.000000 | 5.333333 | 8.708333 | 7.791667 | 103.416667 | 126.333333 | 150.083333 | 141.791667 | 17.041667 | 13.041667 | 8.666667 | 13.250000 |
7 | 2021.02.25 | -6.250000 | -4.208333 | -2.208333 | 1.250000 | -9.875000 | -6.291667 | -5.250000 | -1.583333 | -4.791667 | -2.875000 | -1.541667 | 1.250000 | -9.875000 | -6.291667 | -5.250000 | -1.583333 | 25.000000 | 16.708333 | 22.208333 | 14.791667 | 83.333333 | 86.166667 | 93.541667 | 100.000000 | 88.041667 | 87.125000 | 87.708333 | 99.166667 | 0.295833 | 0.037500 | 0.820833 | 2.566667 | 1016.500000 | 1014.375000 | 1000.750000 | 991.708333 | -4.875000 | -3.208333 | -2.166667 | 1.125000 | 5.333333 | 8.708333 | 7.791667 | 6.166667 | 126.333333 | 150.083333 | 141.791667 | 266.625000 | 13.041667 | 8.666667 | 13.250000 | 10.250000 |
8 | 2021.02.26 | -4.208333 | -2.208333 | 1.250000 | -1.166667 | -6.291667 | -5.250000 | -1.583333 | -4.666667 | -2.875000 | -1.541667 | 1.250000 | -0.916667 | -6.291667 | -5.250000 | -1.583333 | -4.666667 | 16.708333 | 22.208333 | 14.791667 | 15.583333 | 86.166667 | 93.541667 | 100.000000 | 99.625000 | 87.125000 | 87.708333 | 99.166667 | 98.666667 | 0.037500 | 0.820833 | 2.566667 | 1.375000 | 1014.375000 | 1000.750000 | 991.708333 | 1005.916667 | -3.208333 | -2.166667 | 1.125000 | -0.916667 | 8.708333 | 7.791667 | 6.166667 | 2.083333 | 150.083333 | 141.791667 | 266.625000 | 273.625000 | 8.666667 | 13.250000 | 10.250000 | 10.541667 |
9 | 2021.03.01 | -2.208333 | 1.250000 | -1.166667 | -4.208333 | -5.250000 | -1.583333 | -4.666667 | -9.708333 | -1.541667 | 1.250000 | -0.916667 | -3.250000 | -5.250000 | -1.583333 | -4.666667 | -9.708333 | 22.208333 | 14.791667 | 15.583333 | 41.791667 | 93.541667 | 100.000000 | 99.625000 | 95.958333 | 87.708333 | 99.166667 | 98.666667 | 93.125000 | 0.820833 | 2.566667 | 1.375000 | 1.395833 | 1000.750000 | 991.708333 | 1005.916667 | 1004.291667 | -2.166667 | 1.125000 | -0.916667 | -3.333333 | 7.791667 | 6.166667 | 2.083333 | 3.166667 | 141.791667 | 266.625000 | 273.625000 | 259.041667 | 13.250000 | 10.250000 | 10.541667 | 22.958333 |
ax = lag.plot(x="Date", y="humidity(t-3)", kind="bar")
lag.plot(x="Date", y="humidity(t-2)", kind="bar", ax=ax, color="C2")
lag.plot(x="Date", y="humidity(t-1)", kind="bar", ax=ax, color="C3")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17c9b3d0>
ax = lag.plot(x="Date", y="windspeedKmph(t-3)", kind="bar")
lag.plot(x="Date", y="windspeedKmph(t-2)", kind="bar", ax=ax, color="C2")
lag.plot(x="Date", y="windspeedKmph(t-1)", kind="bar", ax=ax, color="C3")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17a2b490>
Mean values of each column
mean = pd.read_csv('/content/Mean.csv')
mean.head()
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -12.625000 | -13.166667 | -7.833333 | -13.166667 | 24.458333 | 5.458333 | 66.625000 | 0.000000 | 1019.583333 | -8.000000 | 10.000000 | 113.250000 | 13.041667 |
1 | 2021.02.17 | -15.291667 | -8.875000 | -5.708333 | -8.875000 | 16.583333 | 6.666667 | 46.291667 | 0.000000 | 1015.875000 | -5.833333 | 10.000000 | 129.625000 | 7.916667 |
2 | 2021.02.18 | -17.750000 | -13.166667 | -9.375000 | -13.166667 | 17.791667 | 21.666667 | 49.875000 | 0.000000 | 1012.458333 | -10.083333 | 10.000000 | 99.833333 | 8.666667 |
3 | 2021.02.19 | -17.166667 | -22.666667 | -15.041667 | -22.666667 | 34.250000 | 15.208333 | 78.458333 | 0.000000 | 1013.083333 | -15.250000 | 10.000000 | 103.416667 | 17.041667 |
4 | 2021.02.22 | -6.250000 | -9.875000 | -4.791667 | -9.875000 | 25.000000 | 83.333333 | 88.041667 | 0.295833 | 1016.500000 | -4.875000 | 5.333333 | 126.333333 | 13.041667 |
ax = mean.plot(x="Date", y="windspeedKmph", kind="bar")
mean.plot(x="Date", y="DewPointC", kind="bar", ax=ax, color="C2")
mean.plot(x="Date", y="tempC", kind="bar", ax=ax, color="C3")
#mean.plot(x="Date", y="HeatIndexC", kind="bar", ax=ax, color="C4")
#mean.plot(x="Date", y="humidity", kind="bar", ax=ax, color="C4")
#mean.plot(x="Date", y="pressure", kind="bar", ax=ax, color="C5")
plt.show()
mean.columns
Index(['Date', 'DewPointC', 'FeelsLikeC', 'HeatIndexC', 'WindChillC', 'WindGustKmph', 'cloudcover', 'humidity', 'precipMM', 'pressure', 'tempC', 'visibility', 'winddirDegree', 'windspeedKmph'], dtype='object')
mean.plot(x="Date", y=['tempC'], kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b18770690>
mean.plot(x="Date", y=['windspeedKmph'], kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b1872b910>
mean.plot(x="Date", y=['humidity'], kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b18680210>
Standard Deviations of each column
stdev=pd.read_csv('/content/Stdev.csv')
stdev
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -16 | -16 | -10 | -16 | 21 | 0 | 59 | 0.0 | 1018 | -11 | 10 | 109 | 11 |
1 | 2021.02.17 | -20 | -12 | -7 | -12 | 8 | 3 | 33 | 0.0 | 1014 | -8 | 10 | 117 | 4 |
2 | 2021.02.18 | -22 | -18 | -15 | -18 | 8 | 6 | 30 | 0.0 | 1012 | -18 | 10 | 93 | 4 |
3 | 2021.02.19 | -20 | -26 | -20 | -26 | 26 | 3 | 76 | 0.0 | 1012 | -22 | 10 | 95 | 14 |
4 | 2021.02.22 | -13 | -20 | -14 | -20 | 18 | 70 | 82 | 0.0 | 1015 | -15 | 2 | 117 | 12 |
5 | 2021.02.23 | -7 | -10 | -5 | -10 | 8 | 58 | 79 | 0.0 | 1013 | -6 | 5 | 132 | 5 |
6 | 2021.02.24 | -4 | -8 | -5 | -8 | 18 | 66 | 77 | 0.0 | 991 | -8 | 5 | 107 | 11 |
7 | 2021.02.25 | 1 | -3 | 1 | -3 | 6 | 100 | 98 | 1.2 | 990 | -1 | 4 | 180 | 4 |
8 | 2021.02.26 | -6 | -11 | -6 | -11 | 12 | 97 | 95 | 0.4 | 994 | -6 | 2 | 258 | 8 |
9 | 2021.03.01 | -7 | -13 | -6 | -13 | 30 | 89 | 86 | 0.6 | 1002 | -6 | 1 | 253 | 17 |
10 | 2021.03.02 | -8 | -13 | -6 | -13 | 27 | 93 | 93 | 0.5 | 1007 | -7 | 2 | 256 | 13 |
stdev.plot(x="Date", y=['humidity'], kind="bar")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17473810>
Minimum values of each column
pd.read_csv('/content/Min.csv')
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -16 | -16 | -10 | -16 | 21 | 0 | 59 | 0.0 | 1018 | -11 | 10 | 109 | 11 |
1 | 2021.02.17 | -20 | -12 | -7 | -12 | 8 | 3 | 33 | 0.0 | 1014 | -8 | 10 | 117 | 4 |
2 | 2021.02.18 | -22 | -18 | -15 | -18 | 8 | 6 | 30 | 0.0 | 1012 | -18 | 10 | 93 | 4 |
3 | 2021.02.19 | -20 | -26 | -20 | -26 | 26 | 3 | 76 | 0.0 | 1012 | -22 | 10 | 95 | 14 |
4 | 2021.02.22 | -13 | -20 | -14 | -20 | 18 | 70 | 82 | 0.0 | 1015 | -15 | 2 | 117 | 12 |
5 | 2021.02.23 | -7 | -10 | -5 | -10 | 8 | 58 | 79 | 0.0 | 1013 | -6 | 5 | 132 | 5 |
6 | 2021.02.24 | -4 | -8 | -5 | -8 | 18 | 66 | 77 | 0.0 | 991 | -8 | 5 | 107 | 11 |
7 | 2021.02.25 | 1 | -3 | 1 | -3 | 6 | 100 | 98 | 1.2 | 990 | -1 | 4 | 180 | 4 |
8 | 2021.02.26 | -6 | -11 | -6 | -11 | 12 | 97 | 95 | 0.4 | 994 | -6 | 2 | 258 | 8 |
9 | 2021.03.01 | -7 | -13 | -6 | -13 | 30 | 89 | 86 | 0.6 | 1002 | -6 | 1 | 253 | 17 |
10 | 2021.03.02 | -8 | -13 | -6 | -13 | 27 | 93 | 93 | 0.5 | 1007 | -7 | 2 | 256 | 13 |
Maximum values of each column
pd.read_csv('/content/Max.csv')
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -10 | -11 | -6 | -11 | 28 | 22 | 70 | 0.0 | 1021 | -6 | 10 | 117 | 14 |
1 | 2021.02.17 | -13 | -5 | -4 | -5 | 27 | 11 | 57 | 0.0 | 1019 | -4 | 10 | 148 | 13 |
2 | 2021.02.18 | -14 | -8 | -6 | -8 | 27 | 63 | 74 | 0.0 | 1014 | -7 | 10 | 108 | 13 |
3 | 2021.02.19 | -14 | -18 | -13 | -18 | 39 | 57 | 81 | 0.0 | 1014 | -13 | 10 | 118 | 19 |
4 | 2021.02.22 | -3 | -4 | 0 | -4 | 35 | 100 | 96 | 0.8 | 1017 | 0 | 10 | 139 | 17 |
5 | 2021.02.23 | -2 | -3 | -1 | -3 | 26 | 99 | 95 | 0.4 | 1016 | -1 | 10 | 168 | 13 |
6 | 2021.02.24 | 1 | -4 | 0 | -4 | 29 | 100 | 98 | 3.8 | 1013 | 0 | 10 | 197 | 15 |
7 | 2021.02.25 | 2 | 0 | 2 | 0 | 25 | 100 | 100 | 4.2 | 994 | 2 | 7 | 312 | 17 |
8 | 2021.02.26 | 1 | -2 | 1 | -2 | 26 | 100 | 99 | 5.6 | 1019 | 1 | 3 | 299 | 13 |
9 | 2021.03.01 | -1 | -5 | 0 | -5 | 51 | 100 | 98 | 3.3 | 1008 | 0 | 7 | 265 | 28 |
10 | 2021.03.02 | 0 | -4 | 1 | -4 | 45 | 100 | 98 | 2.3 | 1015 | 0 | 2 | 271 | 25 |
Median values of each column
med = pd.read_csv('/content/Median.csv')
med
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -12.5 | -13.0 | -8.0 | -13.0 | 24.5 | 4.0 | 67.0 | 0.00 | 1020.0 | -8.0 | 10.0 | 113.0 | 13.0 |
1 | 2021.02.17 | -14.0 | -9.0 | -6.0 | -9.0 | 11.5 | 7.0 | 46.0 | 0.00 | 1016.0 | -6.0 | 10.0 | 126.0 | 6.0 |
2 | 2021.02.18 | -16.0 | -12.0 | -8.0 | -12.0 | 17.0 | 12.5 | 53.0 | 0.00 | 1012.0 | -8.0 | 10.0 | 100.0 | 8.0 |
3 | 2021.02.19 | -17.0 | -23.0 | -15.0 | -23.0 | 35.0 | 8.0 | 78.0 | 0.00 | 1013.0 | -15.0 | 10.0 | 101.5 | 17.0 |
4 | 2021.02.22 | -5.5 | -9.0 | -4.0 | -9.0 | 26.0 | 81.5 | 87.0 | 0.30 | 1017.0 | -4.0 | 6.0 | 126.5 | 12.0 |
5 | 2021.02.23 | -3.0 | -6.0 | -3.0 | -6.0 | 16.5 | 89.0 | 86.0 | 0.00 | 1014.0 | -4.0 | 10.0 | 148.5 | 9.0 |
6 | 2021.02.24 | -3.0 | -5.0 | -1.0 | -5.0 | 21.0 | 100.0 | 87.5 | 0.25 | 1000.5 | -1.5 | 9.0 | 138.5 | 13.0 |
7 | 2021.02.25 | 1.0 | -1.5 | 1.0 | -1.5 | 13.5 | 100.0 | 99.0 | 2.30 | 992.0 | 1.0 | 6.0 | 276.5 | 9.5 |
8 | 2021.02.26 | -1.0 | -4.0 | 0.0 | -4.0 | 14.0 | 100.0 | 99.0 | 1.05 | 1006.0 | 0.0 | 2.0 | 274.5 | 11.0 |
9 | 2021.03.01 | -5.0 | -11.0 | -4.0 | -11.0 | 42.5 | 96.0 | 92.5 | 1.30 | 1004.0 | -4.0 | 2.5 | 259.0 | 23.0 |
10 | 2021.03.02 | -5.0 | -8.5 | -2.5 | -8.5 | 34.5 | 100.0 | 95.0 | 1.15 | 1013.0 | -3.5 | 2.0 | 260.0 | 21.0 |
ax = med.plot(x="Date", y="windspeedKmph", kind="bar")
med.plot(x="Date", y="DewPointC", kind="bar", ax=ax, color="C2")
med.plot(x="Date", y="tempC", kind="bar", ax=ax, color="C3")
#mean.plot(x="Date", y="HeatIndexC", kind="bar", ax=ax, color="C4")
#med.plot(x="Date", y="humidity", kind="bar", ax=ax, color="C4")
#med.plot(x="Date", y="pressure", kind="bar", ax=ax, color="C5")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b178c9990>
Quartile-1 values of each column
25% data are less than these values
pd.read_csv('/content/Q1.csv')
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -13.25 | -14.00 | -9.00 | -14.00 | 23.00 | 0.00 | 65.00 | 0.000 | 1019.00 | -9.00 | 10.00 | 112.00 | 13.00 |
1 | 2021.02.17 | -16.25 | -11.00 | -7.00 | -11.00 | 9.00 | 5.00 | 43.00 | 0.000 | 1015.00 | -7.00 | 10.00 | 118.75 | 4.00 |
2 | 2021.02.18 | -20.25 | -15.00 | -11.00 | -15.00 | 15.00 | 10.00 | 31.75 | 0.000 | 1012.00 | -12.25 | 10.00 | 97.00 | 7.00 |
3 | 2021.02.19 | -18.00 | -24.00 | -15.25 | -24.00 | 32.75 | 6.00 | 77.00 | 0.000 | 1013.00 | -16.00 | 10.00 | 96.00 | 16.00 |
4 | 2021.02.22 | -8.25 | -13.25 | -7.25 | -13.25 | 20.75 | 75.50 | 85.00 | 0.100 | 1016.00 | -7.25 | 2.00 | 122.75 | 12.00 |
5 | 2021.02.23 | -7.00 | -8.00 | -4.00 | -8.00 | 12.00 | 82.75 | 81.75 | 0.000 | 1014.00 | -4.00 | 7.75 | 144.00 | 7.00 |
6 | 2021.02.24 | -3.25 | -6.00 | -2.00 | -6.00 | 19.75 | 88.75 | 80.00 | 0.000 | 994.00 | -3.00 | 5.00 | 122.00 | 12.00 |
7 | 2021.02.25 | 1.00 | -3.00 | 1.00 | -3.00 | 7.75 | 100.00 | 99.00 | 1.975 | 990.00 | 1.00 | 6.00 | 248.75 | 6.00 |
8 | 2021.02.26 | -1.25 | -5.25 | -1.25 | -5.25 | 13.00 | 100.00 | 99.00 | 0.775 | 998.75 | -1.25 | 2.00 | 264.00 | 9.75 |
9 | 2021.03.01 | -6.00 | -12.00 | -5.00 | -12.00 | 35.00 | 93.00 | 89.00 | 0.975 | 1003.00 | -5.00 | 2.00 | 257.00 | 22.00 |
10 | 2021.03.02 | -6.00 | -11.00 | -5.00 | -11.00 | 29.75 | 96.75 | 94.00 | 0.875 | 1010.75 | -6.00 | 2.00 | 258.00 | 18.75 |
Quartile-3 values of each column
75% data are less than these values
pd.read_csv('/content/Q3.csv')
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -11.00 | -12.00 | -7.00 | -12.00 | 26.00 | 7.75 | 69.00 | 0.000 | 1020.00 | -7.00 | 10 | 115.25 | 14.00 |
1 | 2021.02.17 | -14.00 | -7.75 | -5.00 | -7.75 | 26.00 | 8.25 | 50.25 | 0.000 | 1016.25 | -5.00 | 10 | 141.25 | 13.00 |
2 | 2021.02.18 | -16.00 | -11.75 | -7.00 | -11.75 | 21.50 | 29.50 | 64.00 | 0.000 | 1013.00 | -7.00 | 10 | 102.00 | 10.25 |
3 | 2021.02.19 | -16.00 | -21.75 | -13.75 | -21.75 | 36.00 | 17.75 | 80.00 | 0.000 | 1013.25 | -13.75 | 10 | 109.25 | 18.25 |
4 | 2021.02.22 | -3.75 | -6.00 | -1.75 | -6.00 | 27.25 | 91.25 | 92.00 | 0.425 | 1017.00 | -1.75 | 8 | 129.25 | 14.00 |
5 | 2021.02.23 | -2.00 | -4.75 | -2.00 | -4.75 | 20.50 | 93.25 | 93.00 | 0.000 | 1015.00 | -2.00 | 10 | 158.25 | 10.00 |
6 | 2021.02.24 | -1.75 | -4.75 | -0.75 | -4.75 | 24.25 | 100.00 | 95.50 | 1.350 | 1006.50 | -0.75 | 10 | 151.25 | 14.00 |
7 | 2021.02.25 | 1.25 | 0.00 | 1.25 | 0.00 | 22.25 | 100.00 | 100.00 | 2.975 | 993.00 | 1.25 | 7 | 296.00 | 14.25 |
8 | 2021.02.26 | 0.00 | -3.00 | 0.25 | -3.00 | 15.25 | 100.00 | 99.00 | 1.700 | 1013.25 | 0.25 | 2 | 278.25 | 11.00 |
9 | 2021.03.01 | -2.75 | -7.75 | -1.75 | -7.75 | 49.00 | 100.00 | 98.00 | 1.650 | 1005.25 | -1.75 | 5 | 261.00 | 24.00 |
10 | 2021.03.02 | -2.00 | -6.00 | 0.00 | -6.00 | 40.25 | 100.00 | 97.00 | 1.600 | 1014.00 | -1.00 | 2 | 261.00 | 22.25 |
Kurtosis values of each column
krt = pd.read_csv('/content/Kurtosis.csv')
krt
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -0.533679 | -0.329786 | -1.261969 | -0.329786 | -0.810169 | 0.562268 | 0.882804 | NaN | -0.537118 | -1.187185 | NaN | -0.651845 | 0.307322 |
1 | 2021.02.17 | -0.156483 | -0.857302 | -1.169816 | -0.857302 | -1.953784 | -0.838175 | 0.062562 | NaN | 0.371681 | -1.151494 | NaN | -1.754513 | -1.902038 |
2 | 2021.02.18 | -1.416625 | -0.799192 | -0.757343 | -0.799192 | -0.492789 | 0.360928 | -1.677883 | NaN | -0.114172 | -0.370874 | NaN | -0.457923 | -0.417035 |
3 | 2021.02.19 | -0.277862 | -0.315661 | 0.466853 | -0.315661 | 0.994396 | 1.940711 | -1.446053 | NaN | -0.424240 | 1.696292 | NaN | -1.070540 | -0.827527 |
4 | 2021.02.22 | -0.534317 | -0.573204 | -0.057326 | -0.573204 | -0.780388 | -1.174976 | -1.083902 | -0.679238 | -0.158495 | 0.368871 | -1.869970948 | -0.206633 | 1.167653 |
5 | 2021.02.23 | -1.666446 | -1.103563 | -1.109776 | -1.103563 | -0.963271 | 1.227880 | -1.776409 | 8.520248 | 0.126171 | -1.216828 | -0.54550424 | -0.628107 | -0.747762 |
6 | 2021.02.24 | -0.467994 | 0.718575 | 0.431601 | 0.718575 | -0.533456 | 1.013284 | -1.630926 | 0.745217 | -1.328114 | 0.867099 | -1.92742611 | -0.272704 | -1.123266 |
7 | 2021.02.25 | 1.841379 | 0.366071 | 1.841379 | 0.366071 | -1.672208 | 25.000000 | 24.940672 | -0.793899 | 24.997172 | 2.637735 | 4.293262621 | 9.807289 | -1.581365 |
8 | 2021.02.26 | 0.794306 | 1.007814 | 0.862517 | 1.007814 | 1.157910 | 4.163657 | 11.677587 | 9.096371 | -1.434763 | 0.862517 | 9.123966942 | 0.529117 | -0.305705 |
9 | 2021.03.01 | -1.145131 | -1.162153 | -1.114753 | -1.162153 | -1.472484 | -1.288554 | -1.806529 | 2.524619 | -0.666306 | -1.139070 | -0.995033766 | -0.442172 | 1.963088 |
10 | 2021.03.02 | -1.097932 | -1.471421 | -1.711587 | -1.471421 | -1.433854 | 0.283430 | -1.200414 | -0.477294 | -0.239486 | -1.685670 | #DIV/0! | 3.270964 | -0.415955 |
ax = krt.plot(x="Date", y="windspeedKmph", kind="bar")
krt.plot(x="Date", y="DewPointC", kind="bar", ax=ax, color="C2")
krt.plot(x="Date", y="tempC", kind="bar", ax=ax, color="C3")
krt.plot(x="Date", y="humidity", kind="bar", ax=ax, color="C4")
krt.plot(x="Date", y="pressure", kind="bar", ax=ax, color="C5")
plt.show()
ax = med.plot(x="Date", y="windspeedKmph", kind="bar")
med.plot(x="Date", y="DewPointC", kind="bar", ax=ax, color="C2")
med.plot(x="Date", y="tempC", kind="bar", ax=ax, color="C3")
<matplotlib.axes._subplots.AxesSubplot at 0x7f5b17518990>
Skewness values of each column
skw=pd.read_csv('/content/Skewness.csv')
skw
Date | DewPointC | FeelsLikeC | HeatIndexC | WindChillC | WindGustKmph | cloudcover | humidity | precipMM | pressure | tempC | visibility | winddirDegree | windspeedKmph | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021.02.16 | -0.467567 | -0.714413 | -0.105969 | -0.714413 | -0.217241 | 1.201579 | -0.928002 | NaN | -0.442597 | -0.224721 | NaN | 0.219575 | -0.847487 |
1 | 2021.02.17 | -1.072579 | 0.336856 | 0.254766 | 0.336856 | 0.266135 | 0.191835 | -0.061076 | NaN | 0.820977 | 0.141968 | NaN | 0.293102 | 0.261574 |
2 | 2021.02.18 | -0.450174 | -0.183492 | -0.627521 | -0.183492 | 0.309021 | 1.199132 | -0.087316 | NaN | 0.873068 | -0.963440 | NaN | -0.099599 | 0.433610 |
3 | 2021.02.19 | 0.160960 | 0.527283 | -1.093804 | 0.527283 | -1.173962 | 1.672922 | 0.074003 | NaN | -0.080121 | -1.427851 | NaN | 0.546657 | -0.350995 |
4 | 2021.02.22 | -0.764065 | -0.730865 | -0.914180 | -0.730865 | 0.214608 | 0.278739 | 0.286287 | 0.453421 | -1.198510 | -1.046806 | 0.024403951 | 0.299184 | 1.387949 |
5 | 2021.02.23 | -0.371272 | 0.110491 | 0.140984 | 0.110491 | 0.175504 | -1.319644 | 0.011990 | 2.882551 | 0.690896 | 0.152368 | -0.869145464 | 0.225103 | -0.047893 |
6 | 2021.02.24 | 0.824710 | -0.744205 | -0.827806 | -0.744205 | 0.707231 | -1.488540 | 0.039961 | 1.405591 | 0.237356 | -1.120812 | -0.281855629 | 0.743278 | -0.114980 |
7 | 2021.02.25 | 1.584456 | 0.719903 | 1.584456 | 0.719903 | 0.118163 | -5.000000 | -4.991446 | 0.398908 | -4.999592 | -0.365955 | -1.748932273 | -2.752590 | 0.109622 |
8 | 2021.02.26 | -1.225706 | -1.439720 | -1.231829 | -1.439720 | 1.564738 | -2.215633 | -3.309750 | 2.688913 | 0.007760 | -1.231829 | 3.219960287 | 0.536353 | -0.264291 |
9 | 2021.03.01 | 0.536174 | 0.610295 | 0.597052 | 0.610295 | -0.282198 | -0.284675 | -0.058178 | 1.381475 | 0.719840 | 0.487355 | 0.565475194 | 0.229592 | -0.237815 |
10 | 2021.03.02 | 0.404805 | 0.084164 | 0.002836 | 0.084164 | 0.211483 | -1.171055 | 0.345090 | 0.546860 | -0.884637 | 0.116039 | #DIV/0! | 1.486381 | -0.694502 |
ax = skw.plot(x="Date", y="windspeedKmph", kind="bar")
skw.plot(x="Date", y="DewPointC", kind="bar", ax=ax, color="C2")
skw.plot(x="Date", y="tempC", kind="bar", ax=ax, color="C3")
skw.plot(x="Date", y="humidity", kind="bar", ax=ax, color="C4")
skw.plot(x="Date", y="pressure", kind="bar", ax=ax, color="C5")
plt.show()
# split a univariate dataset into train/test sets
def train_test_split(data, n_test):
print(n_test)
data = np.array(data)
return data[:-n_test, :], data[-n_test:, :]
# walk-forward validation for univariate data
def walk_forward_validation(params, data, n_test):
predictions = list()
# split dataset
train, test = train_test_split(data, n_test)
# seed history with training dataset
history = [x for x in train]
# step over each time-step in the test set
for i in range(len(test)):
# split test row into input and output columns
testX, testy = test[i, :-1], test[i, -1]
# fit model on history and make a prediction
yhat = xgboost_forecast(params, history, testX)
# store forecast in list of predictions
predictions.append(yhat)
# add actual observation to history for the next loop
history.append(test[i])
# summarize progress
print('>expected=%.1f, predicted=%.1f' % (testy, yhat))
# estimate prediction error
error = balanced_accuracy_score(test[:, -1], predictions)
return error, test[:, -1], predictions
# fit an xgboost model and make a one step prediction
def xgboost_forecast(params, train, testX):
# transform list into array
train = np.array(train)
# split into input and output columns
trainX, trainy = train[:, :-1], train[:, -1]
# fit model
model = XGBClassifier(**params)
model.fit(trainX, trainy)
# make a one-step prediction
yhat = model.predict(np.array([testX]))
return yhat[0]
def objective(trial: Trial, data) -> float:
params = {
"booster": "gbtree",
#"tree_method": "gpu_hist",
"n_estimators": trial.suggest_int("n_estimators", 0, 1000),
"max_depth": trial.suggest_int("max_depth", 2, 10),
"reg_alpha": trial.suggest_int("reg_alpha", 0, 5),
"reg_lambda": trial.suggest_int("reg_lambda", 0, 5),
"min_child_weight": trial.suggest_int("min_child_weight", 0, 5),
"gamma": trial.suggest_int("gamma", 0, 5),
"learning_rate": trial.suggest_loguniform("learning_rate", 0.005, 0.5),
"colsample_bytree": trial.suggest_discrete_uniform(
"colsample_bytree", 0.1, 1, 0.01
),
"nthread": -1,
"use_label_encoder": False,
"eval_metric": "logloss"
}
mae, y, yhat = walk_forward_validation(params, data, 20)
return mae
if not os.path.exists('output'):
os.makedirs('output')
study = optuna.create_study(
direction="maximize",
sampler=TPESampler(seed=1337),
study_name="res",
storage="sqlite:///output/res.db",
load_if_exists=True
)
study.optimize(
lambda trial: objective(trial, data),
n_trials=50,
show_progress_bar=True
)
df = study.trials_dataframe(attrs=("number", "value", "params", "state"))
df.to_csv("/content/res.csv", sep="\t")
print(study.best_trial)
[I 2021-03-28 11:15:04,669] Using an existing study with name 'res' instead of creating a new one.
/usr/local/lib/python3.7/dist-packages/optuna/progress_bar.py:47: ExperimentalWarning:
Progress bar is experimental (supported from v1.2.0). The interface can change in the future.
HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))
20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:12,312] Trial 50 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 948, 'max_depth': 9, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 3, 'gamma': 0, 'learning_rate': 0.23618847306152302, 'colsample_bytree': 0.48}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:20,098] Trial 51 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 846, 'max_depth': 4, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.0695335543898768, 'colsample_bytree': 0.31}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:28,993] Trial 52 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 775, 'max_depth': 7, 'reg_alpha': 4, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.012531665456851368, 'colsample_bytree': 0.25}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:46,246] Trial 53 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 895, 'max_depth': 6, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.02793571058788696, 'colsample_bytree': 0.39}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:46,533] Trial 54 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 2, 'max_depth': 6, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.030311406721817546, 'colsample_bytree': 0.4}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:48,046] Trial 55 finished with value: 0.6333333333333333 and parameters: {'n_estimators': 53, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.03121875535804658, 'colsample_bytree': 0.51}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:15:55,905] Trial 56 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 994, 'max_depth': 8, 'reg_alpha': 3, 'reg_lambda': 2, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.019273024878129135, 'colsample_bytree': 0.12000000000000001}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:08,936] Trial 57 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 1000, 'max_depth': 8, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.018802607877772444, 'colsample_bytree': 0.30000000000000004}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:12,244] Trial 58 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 206, 'max_depth': 6, 'reg_alpha': 2, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.06840953482526449, 'colsample_bytree': 0.4}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:23,014] Trial 59 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 961, 'max_depth': 9, 'reg_alpha': 0, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.02067717977588002, 'colsample_bytree': 0.28}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:30,816] Trial 60 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 798, 'max_depth': 3, 'reg_alpha': 2, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.015086994863196958, 'colsample_bytree': 0.22}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:38,194] Trial 61 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 965, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02291238724343583, 'colsample_bytree': 0.18}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:44,520] Trial 62 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 905, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02485324742140467, 'colsample_bytree': 0.17}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:16:53,080] Trial 63 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 949, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.03735944358958485, 'colsample_bytree': 0.35}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:00,654] Trial 64 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 915, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.04164650659371941, 'colsample_bytree': 0.33}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:06,758] Trial 65 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 858, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.09530050679299276, 'colsample_bytree': 0.26}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:16,034] Trial 66 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 837, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 5, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.09839998533801414, 'colsample_bytree': 0.27}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:26,638] Trial 67 finished with value: 0.7333333333333334 and parameters: {'n_estimators': 936, 'max_depth': 5, 'reg_alpha': 1, 'reg_lambda': 2, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.0352749581192143, 'colsample_bytree': 0.44000000000000006}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:32,599] Trial 68 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 999, 'max_depth': 9, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 2, 'gamma': 0, 'learning_rate': 0.0500624813678248, 'colsample_bytree': 0.14}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:41,780] Trial 69 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 566, 'max_depth': 7, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 1, 'learning_rate': 0.0650756835306638, 'colsample_bytree': 0.30000000000000004}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:17:52,631] Trial 70 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 891, 'max_depth': 8, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.026099443976545924, 'colsample_bytree': 0.36}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:18:07,355] Trial 71 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 815, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.01071511225453022, 'colsample_bytree': 0.36}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:18:21,787] Trial 72 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 797, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.009825302910307702, 'colsample_bytree': 0.36}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:18:33,905] Trial 73 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 668, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.016233885500387173, 'colsample_bytree': 0.38}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:18:46,598] Trial 74 finished with value: 0.7333333333333334 and parameters: {'n_estimators': 743, 'max_depth': 3, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.015558907807666066, 'colsample_bytree': 0.45999999999999996}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:18:58,016] Trial 75 finished with value: 0.6333333333333333 and parameters: {'n_estimators': 675, 'max_depth': 7, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 3, 'learning_rate': 0.16202586638464883, 'colsample_bytree': 0.32}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:19:04,797] Trial 76 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 944, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02483148976263475, 'colsample_bytree': 0.18}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:19:12,238] Trial 77 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 912, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 2, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.04126035936672927, 'colsample_bytree': 0.33999999999999997}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:19:21,684] Trial 78 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 957, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 1, 'min_child_weight': 2, 'gamma': 1, 'learning_rate': 0.040464570951394045, 'colsample_bytree': 0.43000000000000005}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:19:33,715] Trial 79 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 868, 'max_depth': 7, 'reg_alpha': 2, 'reg_lambda': 2, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.006236733512185402, 'colsample_bytree': 0.25}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:19:41,681] Trial 80 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 856, 'max_depth': 4, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.15779215557243803, 'colsample_bytree': 0.21000000000000002}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 [I 2021-03-28 11:19:47,240] Trial 81 finished with value: 0.5666666666666667 and parameters: {'n_estimators': 904, 'max_depth': 8, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 5, 'gamma': 0, 'learning_rate': 0.03287744318123944, 'colsample_bytree': 0.22}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:19:54,848] Trial 82 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 440, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.029643386393377866, 'colsample_bytree': 0.4}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:06,132] Trial 83 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 836, 'max_depth': 5, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.013371305965194973, 'colsample_bytree': 0.24000000000000002}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:12,090] Trial 84 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 471, 'max_depth': 7, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.013882153271478211, 'colsample_bytree': 0.29000000000000004}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:23,572] Trial 85 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 838, 'max_depth': 5, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.010409587417918647, 'colsample_bytree': 0.24000000000000002}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:29,468] Trial 86 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 465, 'max_depth': 7, 'reg_alpha': 0, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.01287343072460215, 'colsample_bytree': 0.29000000000000004}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:38,496] Trial 87 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 838, 'max_depth': 4, 'reg_alpha': 4, 'reg_lambda': 3, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.011060227250247799, 'colsample_bytree': 0.24000000000000002}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:47,035] Trial 88 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 917, 'max_depth': 8, 'reg_alpha': 2, 'reg_lambda': 5, 'min_child_weight': 2, 'gamma': 0, 'learning_rate': 0.05591902734253109, 'colsample_bytree': 0.33999999999999997}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:20:57,096] Trial 89 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 884, 'max_depth': 8, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.02713438633803257, 'colsample_bytree': 0.31}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:03,997] Trial 90 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 880, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.08578622676816676, 'colsample_bytree': 0.33}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:11,364] Trial 91 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 958, 'max_depth': 3, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.08648625582370155, 'colsample_bytree': 0.33}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:18,284] Trial 92 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 902, 'max_depth': 2, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.11295705740428628, 'colsample_bytree': 0.37}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:23,723] Trial 93 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 365, 'max_depth': 6, 'reg_alpha': 3, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.019708557641065303, 'colsample_bytree': 0.27}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:33,036] Trial 94 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 856, 'max_depth': 4, 'reg_alpha': 1, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 0, 'learning_rate': 0.04677483462800928, 'colsample_bytree': 0.35}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:40,837] Trial 95 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 722, 'max_depth': 7, 'reg_alpha': 2, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.01825717635468616, 'colsample_bytree': 0.22}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:21:51,368] Trial 96 finished with value: 0.7666666666666666 and parameters: {'n_estimators': 787, 'max_depth': 6, 'reg_alpha': 2, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.01444236005266124, 'colsample_bytree': 0.30000000000000004}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:22:06,768] Trial 97 finished with value: 0.6333333333333333 and parameters: {'n_estimators': 798, 'max_depth': 6, 'reg_alpha': 1, 'reg_lambda': 4, 'min_child_weight': 0, 'gamma': 0, 'learning_rate': 0.007864763252994207, 'colsample_bytree': 0.41000000000000003}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:22:11,772] Trial 98 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 499, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 3, 'min_child_weight': 1, 'gamma': 1, 'learning_rate': 0.010030465536742604, 'colsample_bytree': 0.18}. Best is trial 10 with value: 0.7666666666666666. 20 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 >expected=1.0, predicted=0.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=1.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=1.0 >expected=0.0, predicted=0.0 [I 2021-03-28 11:22:15,432] Trial 99 finished with value: 0.6666666666666667 and parameters: {'n_estimators': 450, 'max_depth': 5, 'reg_alpha': 0, 'reg_lambda': 2, 'min_child_weight': 2, 'gamma': 0, 'learning_rate': 0.03309265638711461, 'colsample_bytree': 0.28}. Best is trial 10 with value: 0.7666666666666666. FrozenTrial(number=10, values=[0.7666666666666666], datetime_start=datetime.datetime(2021, 3, 28, 11, 5, 42, 904560), datetime_complete=datetime.datetime(2021, 3, 28, 11, 5, 49, 938970), params={'colsample_bytree': 0.17, 'gamma': 0, 'learning_rate': 0.027666260129432293, 'max_depth': 8, 'min_child_weight': 1, 'n_estimators': 940, 'reg_alpha': 0, 'reg_lambda': 3}, distributions={'colsample_bytree': DiscreteUniformDistribution(high=1.0, low=0.1, q=0.01), 'gamma': IntUniformDistribution(high=5, low=0, step=1), 'learning_rate': LogUniformDistribution(high=0.5, low=0.005), 'max_depth': IntUniformDistribution(high=10, low=2, step=1), 'min_child_weight': IntUniformDistribution(high=5, low=0, step=1), 'n_estimators': IntUniformDistribution(high=1000, low=0, step=1), 'reg_alpha': IntUniformDistribution(high=5, low=0, step=1), 'reg_lambda': IntUniformDistribution(high=5, low=0, step=1)}, user_attrs={}, system_attrs={}, intermediate_values={}, trial_id=11, state=TrialState.COMPLETE, value=None)
Our model's Weighted accuracy is 0.7666666666666666 or ~76.67%. Now our model can forecast the friction and will label it as '0' if dangerous and '1' if safe for driving.