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ml_preprocessing.py
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ml_preprocessing.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import tensorflow as tf
import tensorflow as tf
from sklearn.metrics import confusion_matrix, classification_report
def univarite_analysis(df, continuous):
""" References - (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html) """
# If the features are continuous
if(continuous):
# Box Plots for single analysis
df.plot(kind='box', figsize=(15, 15), subplots=True, layout=(3, 4))
plt.show()
# Histograms for single analysis
df.plot(kind='hist', bins=25, figsize=(15, 12), subplots=True, layout=(3, 4))
plt.show()
#Line Plots for single analysis
df.plot(figsize=(15, 12), subplots=True, layout=(3, 4))
plt.show()
# If the features are categorical
else:
# Pie plot
plt.figure(figsize=(20, 5))
for feature in df:
plt.subplot(1, 5, categorical_features.index(feature) + 1)
features[feature].value_counts().plot(kind='pie')
plt.show()
def label_analysis(data):
plt.figure(figsize=(8, 8))
data['Label'].value_counts().plot(kind='pie', autopct='%.1f%%')
plt.show()
"""
univarite_analysis(features[continuous_features], True)
univarite_analysis(features[categorical_features], False)
label_analysis(data)
"""
def multivariate_analysis(df):
plt.figure(figsize=(20, 20))
sns.pairplot(df)
plt.show()
def correlation_matrix(df):
""" Outputs the correlation of each factor to each other, making it easier to
see what to keep and what to drop"""
corr = df.corr()
plt.figure(figsize=(18, 15))
sns.heatmap(corr, annot=True, vmin=-1.0, cmap='mako')
plt.show()
def feature_importance():
importance=ada_classifier.feature_importances_
std = np.std([tree.feature_importances_ for tree in ada_classifier.estimators_],
axis=0)
indices = np.argsort(importance)
# Plot the feature importances of the forest
plt.figure()
plt.title("Feature importances")
plt.barh(range(X.shape[1]), importance[indices],
color="b", align="center")
plt.yticks(range(X.shape[1]), colum_names)
plt.ylim([0, X.shape[1]])
plt.show()