mirror of
https://github.com/kuhyx/WUT_Computer_Science.git
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Krzysztof kuhy Rudnicki
be962d0c35
git-subtree-dir: Programming/EARIN git-subtree-mainline:635e287095git-subtree-split:a09c96dd65
113 lines
3.2 KiB
Python
113 lines
3.2 KiB
Python
"""
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Program that predicts wine quality based on variant2.csv data
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"""
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import pandas as pd
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import seaborn as sns
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, accuracy_score, f1_score
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from sklearn.linear_model import LogisticRegression
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class LinearRegression:
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"""Implements Linear regression method"""
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def __init__(self):
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self.theta = None
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def fit(self, x_values, y_values):
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"""
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Fit linear regression model to our training data
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"""
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# Add a column of ones to X for the intercept term
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x_values = np.concatenate((np.ones((x_values.shape[0], 1)), y_values), axis=1)
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# Compute the least squares solution using the normal equation
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self.theta = (
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np.linalg.inv(x_values.T.dot(x_values)).dot(x_values.T).dot(y_values)
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)
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def predict(self, x_values):
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"""
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Predict target values for our input data using the trained linear regression model.
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"""
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# Add a column of ones to X for the intercept term
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x_values = np.concatenate((np.ones((x_values.shape[0], 1)), x_values), axis=1)
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# Make predictions using the learned weights
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y_predicted = x_values.dot(self.theta)
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return y_predicted
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def score(self, x_values, y_values):
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"""
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Compute the R-squared score of the linear regression model on our test data.
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"""
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y_predicted = self.predict(x_values)
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ss_res = np.sum((y_values - y_predicted) ** 2)
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ss_tot = np.sum((y_values - np.mean(y_values)) ** 2)
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r2_score = 1 - (ss_res / ss_tot)
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return r2_score
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wine_df = pd.read_csv("variant2.csv")
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wine_df.head()
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wine_df.describe()
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wine_df.info()
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X = wine_df.iloc[:, :-1].values
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y = wine_df.iloc[:, -1].values
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
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scaler = StandardScaler()
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X_train = scaler.fit_transform(X_train)
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X_test = scaler.transform(X_test)
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regressor = LinearRegression()
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regressor.fit(X_train, y_train)
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y_pred = regressor.predict(X_test)
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mse = mean_squared_error(y_test, y_pred)
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print("MSE:", mse)
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classifier = LogisticRegression()
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classifier.fit(X_train, y_train)
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y_pred = classifier.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print("Accuracy:", accuracy)
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y_pred_train = regressor.predict(X_train)
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train_mse = mean_squared_error(y_train, y_pred_train)
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print("Training MSE:", train_mse)
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train_r_squared = regressor.score(X_train, y_train)
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print("Training R^2:", train_r_squared)
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test_r_squared = regressor.score(X_test, y_test)
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print("Testing R^2:", test_r_squared)
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y_pred_train = classifier.predict(X_train)
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train_accuracy = accuracy_score(y_train, y_pred_train)
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print("Training Accuracy:", train_accuracy)
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train_f1_score = f1_score(y_train, y_pred_train, average="weighted")
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print("Training F1 Score:", train_f1_score)
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test_f1_score = f1_score(y_test, y_pred, average="weighted")
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print("Testing F1 Score:", test_f1_score)
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Data1 = sns.countplot(x="quality", data=wine_df)
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plt.draw()
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plt.waitforbuttonpress(0)
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plt.close()
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Data2 = sns.heatmap(wine_df.corr(), annot=True)
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plt.draw()
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plt.waitforbuttonpress(0)
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plt.close()
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