emredjan

Param
  (
     [parameter(Position=0, Mandatory=$true)]
     [String]
     $env_name
  )

# simulate activation of Conda environment
$cenv = "C:\Path\to\anaconda\installation\envs\$env_name"
$Env:CONDA_PREFIX = $cenv

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'''
Load Yelp JSON files and spit out CSV files
Does not try to reinvent the wheel and uses pandas json_normalize
Kinda hacky and requires a bit of RAM. But works, albeit naively.

Tested with Yelp JSON files in dataset challenge round 12:
https://www.yelp.com/dataset/challenge
'''

import json

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plot_lm_4 = plt.figure(4)
plot_lm_4.set_figheight(8)
plot_lm_4.set_figwidth(12)

plt.scatter(model_leverage, model_norm_residuals, alpha=0.5)
sns.regplot(model_leverage, model_norm_residuals, 
            scatter=False, 
            ci=False, 
            lowess=True,
            line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})

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plot_lm_3 = plt.figure(3)
plot_lm_3.set_figheight(8)
plot_lm_3.set_figwidth(12)

plt.scatter(model_fitted_y, model_norm_residuals_abs_sqrt, alpha=0.5)
sns.regplot(model_fitted_y, model_norm_residuals_abs_sqrt, 
            scatter=False, 
            ci=False, 
            lowess=True,
            line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})

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QQ = ProbPlot(model_norm_residuals)
plot_lm_2 = QQ.qqplot(line='45', alpha=0.5, color='#4C72B0', lw=1)

plot_lm_2.set_figheight(8)
plot_lm_2.set_figwidth(12)

plot_lm_2.axes[0].set_title('Normal Q-Q')
plot_lm_2.axes[0].set_xlabel('Theoretical Quantiles')
plot_lm_2.axes[0].set_ylabel('Standardized Residuals');

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plot_lm_1 = plt.figure(1)
plot_lm_1.set_figheight(8)
plot_lm_1.set_figwidth(12)

plot_lm_1.axes[0] = sns.residplot(model_fitted_y, 'mpg', data=auto, 
                          lowess=True, 
                          scatter_kws={'alpha': 0.5}, 
                          line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})

plot_lm_1.axes[0].set_title('Residuals vs Fitted')

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# fitted values (need a constant term for intercept)
model_fitted_y = model_fit.fittedvalues

# model residuals
model_residuals = model_fit.resid

# normalized residuals
model_norm_residuals = model_fit.get_influence().resid_studentized_internal

# absolute squared normalized residuals

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model_f = 'mpg ~ cylinders + \
                 displacement + \
                 horsepower + \
                 weight + \
                 acceleration + \
                 year + \
                 origin'

model = smf.ols(formula=model_f, data=auto)
model_fit = model.fit()

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auto = pd.read_csv('Auto.csv', na_values=['?'])
auto.dropna(inplace=True)
auto.reset_index(drop=True, inplace=True)

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%matplotlib inline

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf

from statsmodels.graphics.gofplots import ProbPlot