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Diffstat (limited to 'src/windows/plotviewer.py')
| -rw-r--r-- | src/windows/plotviewer.py | 266 |
1 files changed, 266 insertions, 0 deletions
diff --git a/src/windows/plotviewer.py b/src/windows/plotviewer.py new file mode 100644 index 0000000..da3886e --- /dev/null +++ b/src/windows/plotviewer.py @@ -0,0 +1,266 @@ +import datetime +import os + +import tkinter +from tkinter import ttk + +import customtkinter +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg +from matplotlib.figure import Figure + +from helpers.subreddits import SUBREDDITS +from .modeltrainer import ModelTrainer +import numpy as np + + +def pretty_number(number): + # Convert number to in B, M, K format + if number >= 1000000000: + return '{:.2f} B'.format(number / 1000000000) + elif number >= 1000000: + return '{:.2f} M'.format(number / 1000000) + elif number >= 1000: + return '{:.2f} K'.format(number / 1000) + else: + return number + +# Author Scoring Function +def author_scores(df): + df_author = df[['author', 'score', 'subreddit', 'num_comments', 'upvote_ratio']] + df_author['final_score'] = df_author['score'] * df_author['upvote_ratio'] + df_author['num_comments'] + df_author = df_author.groupby(['author', 'subreddit']).sum() + df_author = df_author.reset_index() + return df_author + +# Plot Viewer Window +class PlotViewer(customtkinter.CTk): + def __init__(self, posts): + super().__init__() + self.title('Reddit Data Analysis - Plot Viewer') + posx = int(self.winfo_screenwidth() / 2 - 600) + posy = int(self.winfo_screenheight() / 2 - 400) + self.geometry(f'1200x800+{posx}+{posy}') + self.posts = posts + self.create_tabs() + + def create_tabs(self): + self.tabview = customtkinter.CTkTabview(self) + self.tabview.add("Posts") + self.tabview.add("Subscribers") + self.tabview.add("Author Activity") + self.tabview.add("Multi-Subreddit Analysis") + self.tabview.add("Posts per Day") + self.tabview.add("Top 10 Authors") + self.tabview.add("Best Time Analysis") + self.tabview.add("Scores Boxplot") + self.tabview.add("Scores vs Comments") + self.tabview.add("View Data / Predictions") + + fig = Figure(figsize=(12, 8), dpi=72) + self.posts_plot = fig.add_subplot(111) + self.posts_plot.set_title('Number of posts per subreddit') + self.posts_plot.set_xlabel('Subreddit') + self.posts_plot.set_xticklabels(np.arange(len(SUBREDDITS)), rotation=45) + self.posts_plot.set_ylabel('Number of posts') + sns.countplot(x='subreddit', data=self.posts, ax=self.posts_plot) + for p in self.posts_plot.patches: + self.posts_plot.annotate('{:1.0f} posts'.format(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), + ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points') + self.posts_plot.figure.tight_layout() + self.posts_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Posts")) + self.posts_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + fig = Figure(figsize=(12, 8), dpi=72) + self.subscribers_plot = fig.add_subplot(111) + self.subscribers_plot.set_title('Number of subscribers per subreddit') + self.subscribers_plot.set_xlabel('Subreddit') + self.subscribers_plot.set_xticklabels(np.arange(len(SUBREDDITS)), rotation=45) + self.subscribers_plot.set_ylabel('Number of subscribers') + sns.barplot(x='subreddit', y='subreddit_subscribers', data=self.posts, ax=self.subscribers_plot) + for p in self.subscribers_plot.patches: + self.subscribers_plot.annotate('{}'.format(pretty_number(p.get_height())), (p.get_x() + p.get_width() / 2., p.get_height()), + ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points') + self.subscribers_plot.figure.tight_layout() + self.subscribers_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Subscribers")) + self.subscribers_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + fig = Figure(figsize=(12, 8), dpi=72) + self.author_activity_plot = fig.add_subplot(111) + self.author_activity_plot.set_title('Authors Posting in multiple Subreddits') + n_subreddits = self.posts.groupby('author')['subreddit'].nunique() + sns.countplot(x=n_subreddits, palette=sns.color_palette("husl"), ax=self.author_activity_plot) + for p in self.author_activity_plot.patches: + self.author_activity_plot.annotate('{:1.0f} authors'.format(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), + ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points') + self.author_activity_plot.set_xlabel('Number of Subreddits') + self.author_activity_plot.set_ylabel('Number of Authors') + self.author_activity_plot.figure.tight_layout() + self.author_activity_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Author Activity")) + self.author_activity_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + fig = Figure(figsize=(12, 8), dpi=72) + n_upvotes = self.posts.groupby('author')['ups'].sum() + self.multi_subreddit_plot = fig.add_subplot(111) + self.multi_subreddit_plot.set_title('Does posting in multiple subreddits drives more upvotes?') + sns.barplot(x=n_subreddits, y=n_upvotes, palette=sns.color_palette("pastel"), ax=self.multi_subreddit_plot) + for p in self.multi_subreddit_plot.patches: + self.multi_subreddit_plot.annotate('{:1.0f} upvotes'.format(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), + ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points') + self.multi_subreddit_plot.set_xlabel('Number of Subreddits') + self.multi_subreddit_plot.set_ylabel('Number of Upvotes') + self.multi_subreddit_plot.set_xticks(list(range(0, len(n_subreddits.unique()))), list(map(lambda x: '{} Subreddits'.format(x) if x > 1 else '{} Subreddits'.format(x), list(range(1, len(n_subreddits.unique()) + 1))))) + self.multi_subreddit_plot.figure.tight_layout() + self.multi_subreddit_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Multi-Subreddit Analysis")) + self.multi_subreddit_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + + ppd_df = self.posts.groupby(['subreddit', 'created_utc']).size().reset_index(name='counts') + ppd_df['created_utc'] = pd.to_datetime(ppd_df['created_utc']).dt.date + ppd_df = ppd_df.groupby(['subreddit', 'created_utc']).sum().reset_index() + ppd_df = ppd_df.pivot(index='created_utc', columns='subreddit', values='counts') + ppd_df = ppd_df.fillna(0) + last_6M = datetime.date.today() - datetime.timedelta(days=180) + ppd_df = ppd_df.loc[ppd_df.index >= last_6M] + palette = sns.color_palette("dark6", len(SUBREDDITS)) + + + fig, axes = plt.subplots(5, 3, figsize=(20, 20), dpi=24) + fig.suptitle('Number of posts per day per subreddit (Last 6 Months)\n', fontsize=16) + fig.subplots_adjust(hspace=0.5, wspace=0.5) + for i, subreddit in enumerate(SUBREDDITS): + ax = axes[i // 3, i % 3] + ax.set_title(subreddit) + ax.set_xlabel('Date') + ax.set_ylabel('Number of Posts') + ax.set_xticklabels(ppd_df.index, rotation=0) + sns.lineplot(data=ppd_df[subreddit], ax=ax, color=palette[i]) + self.ppd_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Posts per Day")) + self.ppd_plot.figure.tight_layout() + self.ppd_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + top_10_authors_per_subreddit = author_scores(self.posts).groupby('subreddit').apply(lambda x: x.nlargest(10, 'final_score')) + top_10_authors_per_subreddit = top_10_authors_per_subreddit.reset_index(drop=True) + fig, axes = plt.subplots(5, 3, figsize=(20, 20), dpi=24) + fig.suptitle('Top 10 Authors per Subreddit\n', fontsize=16) + fig.subplots_adjust(hspace=0.5, wspace=0.5) + for i, subreddit in enumerate(SUBREDDITS): + ax = axes[i // 3, i % 3] + ax.set_title(subreddit) + ax.set_xticklabels(axes[i//3, i%3].get_xticklabels(), rotation=30, horizontalalignment='right') + sns.barplot(x='author', y='final_score', data=top_10_authors_per_subreddit[top_10_authors_per_subreddit['subreddit'] == subreddit], ax=ax, palette=sns.color_palette("pastel", 10)) + ax.set_ylabel('Final Score') + ax.set_xlabel('') + for p in axes[i//3, i%3].patches: + axes[i//3, i%3].annotate('{:1.0f}'.format(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()), + ha = 'center', va = 'center', xytext = (0, 10), textcoords = 'offset points') + self.top_10_authors_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Top 10 Authors")) + self.top_10_authors_plot.figure.tight_layout() + self.top_10_authors_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + # Finding the best time to post on each subreddit + best_time_df = self.posts[['subreddit', 'created_utc', 'score', 'num_comments']] + best_time_df['final_score'] = best_time_df['score'] + best_time_df['num_comments'] + best_time_df.drop(['score', 'num_comments'], axis=1, inplace=True) + + # Convert the created_utc column to datetime + best_time_df['created_utc'] = pd.to_datetime(best_time_df['created_utc']) + best_time_df['day'] = best_time_df['created_utc'].dt.day_name() + best_time_df['hour'] = best_time_df['created_utc'].dt.hour + best_time_df.drop('created_utc', axis=1, inplace=True) + + # Find total engagement per hour + best_time_df = best_time_df.groupby(['subreddit', 'day', 'hour']).sum() + best_time_df = best_time_df.reset_index() + days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'] + best_time_df['day'] = pd.Categorical(best_time_df['day'], categories=days, ordered=True) + + # Plotting the best time to post on each subreddit + fig, axes = plt.subplots(5, 3, figsize=(20, 20), dpi=24) + fig.suptitle('Best Time to Post on Each Subreddit\n', fontsize=20) + for i, subreddit in enumerate(best_time_df['subreddit'].unique()): + sns.lineplot(x='hour', y='final_score', hue='day', data=best_time_df[best_time_df['subreddit'] == subreddit], ax=axes[i//3, i%3], palette=sns.color_palette("husl", 7)) + axes[i//3, i%3].set_title(subreddit) + axes[i//3, i%3].set_xticks(range(0, 24)) + axes[i//3, i%3].set_xticklabels(list(map(lambda x: (f'0{x}:00' if x < 10 else f'{x}:00'), list(range(0, 24)))), rotation=45, horizontalalignment='right') + axes[i//3, i%3].set_xlabel('Time of Day') + axes[i//3, i%3].set_ylabel('Total Engagement') + self.best_time_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Best Time Analysis")) + self.best_time_plot.figure.tight_layout() + self.best_time_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + fig = Figure(figsize=(12, 8), dpi=72) + self.scores_boxplot = fig.add_subplot(111) + sns.boxplot(x='subreddit', y='score', data=self.posts, ax=self.scores_boxplot) + self.scores_boxplot.set_title('Boxplot of Scores in Each Subreddit') + self.scores_boxplot.set_xlabel('Subreddit') + self.scores_boxplot.set_ylabel('Score') + self.scores_boxplot = FigureCanvasTkAgg(fig, self.tabview.tab("Scores Boxplot")) + self.scores_boxplot.figure.tight_layout() + self.scores_boxplot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + # Scatterplot of the scores and number of comments in each subreddit + fig, axes = plt.subplots(5, 3, figsize=(20, 20), dpi=24) + fig.suptitle('Scatterplot of Scores and Number of Comments in Each Subreddit\n', fontsize=20) + palette=sns.color_palette("deep", 15) + for i, subreddit in enumerate(self.posts['subreddit'].unique()): + sns.scatterplot(x='score', y='num_comments', data=self.posts[self.posts['subreddit'] == subreddit], ax=axes[i//3, i%3], color=palette[i]) + axes[i//3, i%3].set_title(subreddit) + axes[i//3, i%3].set_xlabel('Score') + axes[i//3, i%3].set_ylabel('Number of Comments') + self.scores_comments_plot = FigureCanvasTkAgg(fig, self.tabview.tab("Scores vs Comments")) + self.scores_comments_plot.figure.tight_layout() + self.scores_comments_plot.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + + + # View Data / Predictions tab + # show the posts dataframe in a table + self.posts_table = ttk.Treeview(self.tabview.tab("View Data / Predictions")) + self.posts_table.pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1) + self.posts_table['columns'] = list(self.posts.columns) + for column in self.posts_table['columns']: + self.posts_table.column(column, anchor='w') + self.posts_table.heading(column, text=column, anchor='w') + + # hide the first column (index) + self.posts_table.column('#0', width=0, stretch=tkinter.NO) + + for i, row in self.posts.iterrows(): + if i < 100: + self.posts_table.insert('', 'end', values=list(row)) + + if not os.path.exists('models') or len(os.listdir('models')) == 0: + try: + os.mkdir('models') + except: + pass + self.models_label = customtkinter.CTkLabel(self.tabview.tab("View Data / Predictions"), text="No models found. Please train the models first.", pady= 10) + self.models_label.pack() + self.models_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Train Models", command=self.train_models) + self.models_button.pack() + else: + self.models_label = customtkinter.CTkLabel(self.tabview.tab("View Data / Predictions"), text="Models found. Predict by entering data on the next screen.", pady= 10) + self.models_label.pack() + self.models_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Predict") + self.models_button.pack() + + self.tabview.pack(expand=True, fill='both') + + def train_models(self): + # open model training child window + mt = ModelTrainer(self, self.posts) + mt.grab_set() + mt.focus_set() + self.wait_window(mt) + + self.models_label.destroy() + self.models_button.destroy() + self.models_label = customtkinter.CTkLabel(self.tabview.tab("View Data / Predictions"), text="Models found. Predict by entering data on the next screen.", pady= 10) + self.models_label.pack() + self.models_button = customtkinter.CTkButton(self.tabview.tab("View Data / Predictions"), text="Predict") + self.models_button.pack() + + |