#!/usr/bin/env python
# coding: utf-8

# <h1 align='center'>Iris Plant Data Modeling</h1>
# <h4 align='center'>by Wilfred Morgan</h4>

# ## Problem Description:
# Predict the flower class based on available attributes.

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from IPython.display import IFrame


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# Flower Classification
IFrame('http://www.spataru.at/iris-dataset-svm/iris_types.jpg', width=1280, height=300)


# ### Problem assumptions:
# * Data is accurate
# * No missing values
# * Data is or can be in a format to be used for machine learning.

# ### Similar problems:
# * Classifying other floral species
# * Classifying cancer cells

# ## Basic Information
# * Number of observations: **150**
# * Number of features: **4**
# * Data types of features: **Numerical**
# * Target variable: **class** *(Categorical)*

# ### Peek at the Data

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# Import Libaries
import pandas as pd
import numpy as np


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# Import Data
data_location = "/Users/wmemorgan/Google Drive/Computer_Data_Science_Lab/iris_data_modeling/data/02_prepared_data/iris.data" 
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
data = pd.read_csv(data_location, names=names)


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data.head()


# ### Class Distribution

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print(data.groupby('class').size())


# ### Other Observations
# * No missing data or corrupted data
# * Sepal length and petal length variables had the strongest correlation with class.

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# Import Libaries
import matplotlib.pyplot as plt
import seaborn as sns
get_ipython().run_line_magic('matplotlib', 'inline')


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f, axes = plt.subplots(2, 2, figsize=(12,8)) #More than one dimension

bp1 = sns.boxplot(data=data, x="class", y="sepal-length", ax=axes[0,0])
bp2 = sns.boxplot(data=data, x="class", y="sepal-width", ax=axes[0,1])
bp3 = sns.boxplot(data=data, x="class", y="petal-length", ax=axes[1,0])
bp4 = sns.boxplot(data=data, x="class", y="petal-width", ax=axes[1,1])


# ## Model Evaluation and Selection
# * Selected a mix of simple linear (LR and LDA), nonlinear (KNN, CART, NB and SVM) algorithms. 
# * Used k-fold cross-validation (CV) procedure to evaluate the accuracy score of each algorithm.
# * Identified k-Nearest Neighbor (k-NN) as the best algorithm to use for modeling this dataset. 
# * **NOTE: Used default parameters for KNNClassifier module**

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# Test options and evaluation metric
seed = 7
scoring = 'accuracy'


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# Import library
from sklearn.model_selection import train_test_split, KFold, cross_val_score

# Assign features and output
X = data.drop('class',axis=1)
y = data['class']
test_size = 0.2
seed = 7


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# Create training and test datasets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = test_size, random_state=seed)


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# Import Algorithm Libraries
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.model_selection import KFold, cross_val_score


# ## Algorithm Evaluation

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# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
    kfold = KFold(n_splits=10, random_state=seed)
    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)


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# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()


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# Fitting the classifier to the Training set
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
predictions = knn.predict(X_test)


# ## Results
# * Prediction model has an accuracy of 90%
# * **Success Factors:**
#     * High quality data (no corrupt or missing data)
#     * k-fold CV for determining the best model

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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix


# ## Accuracy Summary

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print('Accuracy Score:')
print(accuracy_score(y_test,predictions))
print('Confusion Matrix:')
print(confusion_matrix(y_test,predictions))
print('Classification Report:')
print(classification_report(y_test,predictions))


# ## Conclusion
# * Include k-fold CV procedures for supervised classification modeling.
# * Will use similar methodology for regression and clustering modeling.

# <h1 align='center'>THANK YOU</h1>

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