diff options
Diffstat (limited to 'src/windows/predict.py')
| -rw-r--r-- | src/windows/predict.py | 58 |
1 files changed, 31 insertions, 27 deletions
diff --git a/src/windows/predict.py b/src/windows/predict.py index 374ee09..49402ff 100644 --- a/src/windows/predict.py +++ b/src/windows/predict.py @@ -1,24 +1,21 @@ -import random -import math import customtkinter import tkinter import pandas as pd import os import pickle -from scipy.sparse import hstack -from sklearn.feature_extraction.text import TfidfVectorizer -from sklearn.preprocessing import LabelBinarizer from .modeltrainer import preprocess import tkinter.messagebox +import joblib class Predict(customtkinter.CTkToplevel): model_dir = "models/" - def __init__(self, parent): + def __init__(self, parent, selected_model): super().__init__(parent) self.parent = parent self.title("Predictions") self.grab_set() self.focus_set() + self.selected_model = selected_model posx = int(self.winfo_screenwidth()/2 - 150) posy = int(self.winfo_screenheight()/2 - 350) self.geometry("300x650+{}+{}".format(posx, posy)) @@ -29,6 +26,9 @@ class Predict(customtkinter.CTkToplevel): self.title_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w') self.title_entry = customtkinter.CTkEntry(self, width=240) self.title_entry.pack(pady=10, padx=10) + + # uncomment the following lines to add title + self.title_entry.insert(0, 'A good openCV tutorial?') # selftext @@ -36,6 +36,9 @@ class Predict(customtkinter.CTkToplevel): self.selftext_label.pack(pady=5, padx=30, fill='x', side=tkinter.TOP, anchor='w') self.selftext_entry = customtkinter.CTkTextbox(self, width=240, height=100) self.selftext_entry.pack(pady=10, padx=10) + + # uncomment the following lines to add selftext + self.selftext_entry.insert("0.0", "So I'm learning openCV in python, and now I want as a project to develop some score calculator for a scrabble game. I watched this tutorial from codecamp, and i read about of functionalities of opencv module (such as medianblur, gaussianblur, addweighted, Canny, threshold, and so on), but i still can't grasp it together. Like, i know how to blur an image, to reduce noise let's say, but i don't know when to do that, and especially, why and how much, so I'm searching for a good openCV tutorial that explains these situations. \n As an example, yesterday I did a project where i would've get a sudoku box from an image(by getting the top left, top right, bottom left, bottom right corners of the sudoku box). However, when I tried the same code for the project with the scrabble board, it's a total mess.") # subreddit @@ -82,39 +85,40 @@ class Predict(customtkinter.CTkToplevel): day = self.day_entry.get() hour = self.hour_entry.get() hour = int(hour.split(':')[0]) - distinguished = self.distinguished_entry.get() + distinguished = False if self.distinguished_entry.get() == 0 else True if not title or not selftext or not subreddit or not day or not hour: tkinter.messagebox.showerror('Error', 'Please fill all the fields') return # load the model - ups_model = pickle.load(open(os.path.join(self.model_dir, "DummyRegressor_ups.pkl"), 'rb')) - num_comments_model = pickle.load(open(os.path.join(self.model_dir, "DummyRegressor_num_comments.pkl"), 'rb')) + ups_model = pickle.load(open(os.path.join(self.model_dir, self.selected_model + '_ups.pkl'), 'rb')) + num_comments_model = pickle.load(open(os.path.join(self.model_dir, self.selected_model + '_num_comments.pkl'), 'rb')) + + # load the vectorizer + vectorizer = joblib.load(os.path.join(self.model_dir, "vectorizer.pkl")) text = title + " " + selftext text = preprocess(text) - post = pd.DataFrame({ - 'text': [text], - 'subreddit': [subreddit], - 'day': [day], - 'hour': [hour], - 'distinguished': [distinguished] - }) + input_data = pd.DataFrame(columns=['text', 'day', 'hour', 'subreddit', 'distinguished']) + input_data = input_data.append({'text': text, 'day': day, 'hour': hour, 'subreddit': subreddit, 'distinguished': distinguished}, ignore_index=True) + + cat_cols = ['day', 'hour', 'subreddit', 'distinguished'] + for col in cat_cols: + input_data[col] = input_data[col].astype('category') + input_data = pd.get_dummies(input_data, columns=cat_cols) + input_data['text'] = input_data['text'].astype(str) - self.tfidf_vectorizer = TfidfVectorizer() - self.label_binarizer = LabelBinarizer() + X = vectorizer.transform(input_data['text']) - post_cat = [self.label_binarizer.fit_transform(post[col]) for col in ['subreddit', 'day', 'hour', 'distinguished']] - post_text = self.tfidf_vectorizer.fit_transform(post['text']) - postX = hstack([post_text] + post_cat).tocsr() + # predict the ups + ups = ups_model.predict(X) + ups = int(ups[0]) - ups = int(ups_model.predict(postX)[0]) - num_comments = int(num_comments_model.predict(postX)[0]) + # predict the num_comments + num_comments = num_comments_model.predict(X) + num_comments = int(num_comments[0]) - # random bias from the prediction - ups = int(ups // math.log(ups + 1)) - num_comments = int(num_comments // math.log(num_comments + 1)) + tkinter.messagebox.showinfo('Result', 'Predicted ups: {}\nPredicted num_comments: {}'.format(ups, num_comments)) - tkinter.messagebox.showinfo('Predictions', 'Predicted ups: {}\nPredicted num_comments: {}'.format(ups, num_comments)) |