Merge pull request #3 from CAM-Gerlach/cam-improvements-solutions

PR: Improve code correctness, style, performance and format in solutions

workshop_solutions.py

@@ -1,85 +1,88 @@
 # -*- coding: utf-8 -*-
-#
-# Copyright © Spyder Project Contributors
-# Licensed under the terms of the MIT License
 """Workshop main flow."""
 
+# pylint: disable=invalid-name, fixme
 
-# In[1] Importing Libraries and Data
+
+# %% [1] Importing Libraries and Data
 
 # Third-party imports
 import matplotlib.pyplot as plt
 import pandas as pd
-from sklearn.model_selection import train_test_split
 from sklearn import linear_model
 from sklearn.metrics import explained_variance_score
+from sklearn.model_selection import train_test_split
 
 # Local imports
-from utils import (
-    plot_correlations, plot_color_gradients, aggregate_by_year,
-    predicted_temperature)
+from utils import aggregate_by_year, plot_correlations, plot_color_gradients
+
 
-# In[2] Exploring Data
+# %% [2] Exploring the Data
 
-# Reading data
+# Read the data
 weather_data = pd.read_csv('data/weatherHistory.csv')
 
-# Print size of data 
-print(len(weather_data))
-# Print first 3 rows of DataFrame
-print(weather_data.head(3))
+# Print length of data
+len(weather_data)
 
-# TO DO: Print the last 3 rows of the DataFrame
-print(weather_data.tail(3))
+# Print first three rows of DataFrame
+weather_data.head(3)
 
+# TO DO: Print the last three rows of the DataFrame
+weather_data.tail(3)
 
-# In[3] Visualisation
+
+# %% [3] Visualization
 
 # Order rows according to date
+weather_data = pd.read_csv('data/weatherHistory.csv')
 weather_data['Formatted Date'] = pd.to_datetime(
-    weather_data['Formatted Date'])
+    weather_data['Formatted Date'].str[:-6])
 weather_data_ordered = weather_data.sort_values(by='Formatted Date')
-# Order Index according to date
-weather_data_ordered = weather_data_ordered.reset_index(drop=True)
+
+# Reset index to restore its order
+weather_data_ordered.reset_index(drop=True)
+
 # Drop categorical columns
-weather_data_ordered = weather_data_ordered.drop(
+weather_data_ordered.drop(
     columns=['Summary', 'Precip Type', 'Loud Cover', 'Daily Summary'])
-# Plot Temperature Vs Formatted Date
+
+# Plot temperature vs. date
 weather_data_ordered.plot(
-    x='Formatted Date', y=['Temperature (C)'], color='red', figsize=(15, 8))
+    x='Formatted Date', y='Temperature (C)', color='red', figsize=(15, 8))
 
-# TO DO: Plot Temperature (C) V.S the Date using only the data from 2006
-weather_data_ordered.head(8759).plot(x='Formatted Date', y=['Temperature (C)'], color='red')
+# TODO: Plot temperature vs date using only the data from 2006
+weather_data_ordered.loc[
+    weather_data_ordered["Formatted Date"].dt.year == 2006, :].plot(
+        x='Formatted Date', y='Temperature (C)', color='red')
 
-# -----------------------------------------------------------------------------
-# Plot Temperature and Humidity in the same plot
+# Plot temperature and humidity in the same plot
 weather_data_ordered.plot(
     subplots=True, x='Formatted Date', y=['Temperature (C)', 'Humidity'],
     figsize=(15, 8))
-# TO DO: Plot different combinations of the variables, for different years
 
+# TODO: Plot different combinations of the variables, and for different years
 
-# -----------------------------------------------------------------------------
 
-# In[4] Data summarization and aggregation
+# %% [4] Data summarization and aggregation
 
 # Weather data by year
 weather_data_by_year = aggregate_by_year(
-    weather_data_ordered, 'Formatted Date')
+    weather_data_ordered, date_column='Formatted Date')
 
-# TO DO: Create and use a function to get the average
-#       of the weather data by month
+# TODO: Create and use a function to average the weather data by month
 
 
-# In[5] Data Analysis and Interpretation
+# %% [5] Data Analysis and Interpretation
 
-# Plot Correlations
+# Plot correlations
 plot_correlations(weather_data_ordered, size=15)
-# Plot Gradients colormaps
-cmap_category, cmap_list = ('Plot gradients convention', ['viridis', ])
-plot_color_gradients(cmap_category, cmap_list)
 
-# Compute Correlations 
+# Plot gradient colormaps
+plot_color_gradients(
+    cmap_category='Plot gradients convention', cmap_list=['viridis', ])
+
+# Compute correlations
 weather_correlations = weather_data_ordered.corr()
 weather_data_ordered['Temperature (C)'].corr(
     weather_data_ordered['Humidity'])
@@ -88,35 +91,33 @@
 #       Contrast them with the weather_correlations dataframe
 
 
-# In[6] Data Modeling and Prediction
+# %% [6] Data Modeling and Prediction
 
 # Get data subsets for the model
-X_train, X_test, Y_train, Y_test = train_test_split(
+x_train, x_test, y_train, y_test = train_test_split(
     weather_data_ordered['Humidity'], weather_data_ordered['Temperature (C)'],
     test_size=0.25)
 
 # Run regression
-regresion = linear_model.LinearRegression()
-regresion.fit(X_train.values.reshape(-1, 1), Y_train.values.reshape(-1, 1))
-print(regresion.intercept_, regresion.coef_)  # beta_0=intercept, beta_1=coef_
-
-# Get coefficients
-beta_0 = regresion.intercept_[0]
-beta_1 = regresion.coef_[0, 0]
-
-# Plot predicted model with test data.
-Y_predict = predicted_temperature(X_test, beta_0, beta_1)
-plt.scatter(X_test, Y_test, c='red', label='observation', s=1)
-plt.scatter(X_test, Y_predict, c='blue', label='model')
+regression = linear_model.LinearRegression()
+regression.fit(x_train.values.reshape(-1, 1), y_train.values.reshape(-1, 1))
+
+# Print coefficients
+print(regression.intercept_, regression.coef_)  # beta_0, beta_1
+
+
+# %% [7] Predictive Model Testing and Evaluation
+
+# Plot predicted model with test data
+y_predict = regression.predict(x_test.values.reshape(-1, 1))
+plt.scatter(x_test, y_test, c='red', label='Observation', s=1)
+plt.scatter(x_test, y_predict, c='blue', label='Model')
 plt.xlabel('Humidity')
 plt.ylabel('Temperature (C)')
 plt.legend()
 plt.show()
 
-# TO DO: Using the coefficients predict the temperature for a
-#       given level of humidity using the 'predicted_temperature' function
-#       available in 'utils'
+# TODO: Using the model, predict the temperature for a given level of humidity
 
 # Evaluate model numerically
-ev = explained_variance_score(Y_test, Y_predict)
-print(ev)
+explained_variance_score(y_test, y_predict)