Created
November 8, 2020 22:53
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Put in a dictionary r2, equation, and series to plot from simple linear regression for easy use with matplotlib
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| # © 2020 dagrha. GPLv3.0 | |
| def linear_regression(df, x_col, y_col): | |
| from sklearn.linear_model import LinearRegression | |
| from sklearn.metrics import r2_score | |
| x = df[(df[x_col].notnull()) &(df[y_col].notnull())][x_col].values.reshape(-1, 1) | |
| y = df[(df[x_col].notnull()) &(df[y_col].notnull())][y_col].values.reshape(-1, 1) | |
| lr = LinearRegression() | |
| lr.fit(x, y) | |
| x_min_buffer = min(x) * 0.5 | |
| x_max_buffer = max(x) * 1.5 | |
| x_out = np.array([x_min_buffer, x_max_buffer]) | |
| y_out = lr.predict(x_out) | |
| r2 = f'r2: {r2_score(y, lr.predict(x)):0.3f}' | |
| eq = f'{y_col} = {lr.coef_[0][0]:.5E} * {x_col} + {lr.intercept_[0]:.5E}' | |
| return { | |
| 'x': x_out, | |
| 'y': y_out, | |
| 'r2': r2, | |
| 'eq': eq, | |
| } |
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