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| import os | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| import pandas as pd | |
| import tarfile | |
| import urllib.request | |
| DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/" | |
| HOUSING_PATH = os.path.join("datasets", "housing") | |
| HOUSING_URL = DOWNLOAD_ROOT + "datasets/housing/housing.tgz" | |
| def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH): | |
| if not os.path.isdir(housing_path): | |
| os.makedirs(housing_path) | |
| tgz_path = os.path.join(housing_path, "housing.tgz") | |
| urllib.request.urlretrieve(housing_url, tgz_path) | |
| housing_tgz = tarfile.open(tgz_path) | |
| housing_tgz.extractall(path=housing_path) | |
| housing_tgz.close() | |
| def load_housing_data(housing_path=HOUSING_PATH): | |
| csv_path = os.path.join(housing_path, "housing.csv") | |
| return pd.read_csv(csv_path) | |
| #1. Download the data | |
| fetch_housing_data() | |
| housing_pd = load_housing_data() | |
| housing_pd.head() | |
| ## tentatively drop categorical feature | |
| housing = housing_pd.drop('ocean_proximity', axis=1) | |
| housing | |
| #2. Prepare the Data for Machine Learning Algorithms | |
| ## 1. split data to get train and test set | |
| from sklearn.model_selection import train_test_split | |
| train_set, test_set = train_test_split(housing, test_size=0.2, random_state=10) | |
| ## 2. clean the missing values | |
| train_set_clean = train_set.dropna(subset=["total_bedrooms"]) | |
| train_set_clean | |
| ## 2. derive training features and training labels | |
| train_labels = train_set_clean["median_house_value"].copy() # get labels for output label Y | |
| train_features = train_set_clean.drop("median_house_value", axis=1) # drop labels to get features X for training set | |
| ## 4. scale the numeric features in training set | |
| from sklearn.preprocessing import MinMaxScaler | |
| scaler = MinMaxScaler() ## define the transformer | |
| scaler.fit(train_features) ## call .fit() method to calculate the min and max value for each column in dataset | |
| train_features_normalized = scaler.transform(train_features) | |
| train_features_normalized | |
| #3. Training ML model on the Training Set | |
| from sklearn.linear_model import LinearRegression ## import the LinearRegression Function | |
| lin_reg = LinearRegression() ## Initialize the class | |
| lin_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning | |
| ### visualize the data | |
| def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): | |
| path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) | |
| print("Saving figure", fig_id, ' to ',path) | |
| if tight_layout: | |
| plt.tight_layout() | |
| plt.savefig(path, format=fig_extension, dpi=resolution) | |
| PROJECT_ROOT_DIR='./' | |
| IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images") | |
| os.makedirs(IMAGES_PATH, exist_ok=True) | |
| images_path = os.path.join(PROJECT_ROOT_DIR, "images", "end_to_end_project") | |
| os.makedirs(images_path, exist_ok=True) | |
| DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/" | |
| filename = "california.png" | |
| print("Downloading", filename) | |
| url = DOWNLOAD_ROOT + "images/end_to_end_project/" + filename | |
| urllib.request.urlretrieve(url, os.path.join(images_path, filename)) | |
| ### written by Jie | |
| def draw_map_customize(longitude,latitude, fig_id='test',fig_extension='png' ): | |
| import matplotlib.image as mpimg | |
| california_img=mpimg.imread(os.path.join(images_path, filename)) | |
| ax = housing.plot(kind="scatter", x="longitude", y="latitude", figsize=(10,7), | |
| s=housing['population']/100, label="Population", | |
| c="median_house_value", cmap="jet", | |
| colorbar=False, alpha=0.4) | |
| plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5, | |
| cmap=plt.get_cmap("jet")) | |
| plt.ylabel("Latitude", fontsize=18) | |
| plt.xlabel("Longitude", fontsize=18) | |
| plt.xticks(fontsize=18, rotation=0) | |
| plt.yticks(fontsize=18, rotation=0) | |
| plt.plot(longitude,latitude, "ro", alpha=0.7, marker=r'$\clubsuit$', markersize=30) | |
| plt.annotate("Your location is here", xy=(longitude,latitude), xytext=(longitude+1,latitude+1), fontsize=20, | |
| arrowprops=dict(arrowstyle="->")) | |
| prices = housing["median_house_value"] | |
| tick_values = np.linspace(prices.min(), prices.max(), 11) | |
| cbar = plt.colorbar(ticks=tick_values/prices.max()) | |
| cbar.ax.set_yticklabels(["$%dk"%(round(v/1000)) for v in tick_values], fontsize=14) | |
| cbar.set_label('Median House Value', fontsize=16) | |
| plt.legend(fontsize=16) | |
| save_fig(fig_id) | |
| #plt.show() | |
| path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) | |
| return path | |
| def get_sample_data(num_data): | |
| sample_data = [] | |
| for i in range(num_data): | |
| samp = housing.sample(1) | |
| longitude = float(samp['longitude'].values[0]) | |
| latitude = float(samp['latitude'].values[0]) | |
| housing_median_age = float(samp['housing_median_age'].values[0]) | |
| total_rooms = float(samp['total_rooms'].values[0]) | |
| total_bedrooms = float(samp['total_bedrooms'].values[0]) | |
| population = float(samp['population'].values[0]) | |
| households = float(samp['households'].values[0]) | |
| median_income = float(samp['median_income'].values[0]) | |
| sample_data.append([longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income]) | |
| return sample_data | |
| def predict_price(longitude,latitude,housing_median_age,total_rooms,total_bedrooms,population,households,median_income): | |
| #import pickle | |
| #loaded_model = pickle.load(open('KNN_classifier.pickle', 'rb')) | |
| #print(loaded_model) | |
| # initialize data of lists. | |
| data = {'longitude':[float(longitude)], | |
| 'latitude':[float(latitude)], | |
| 'housing_median_age':[float(housing_median_age)], | |
| 'total_rooms':[float(total_rooms)], | |
| 'total_bedrooms':[float(total_bedrooms)], | |
| 'population':[float(population)], | |
| 'households':[float(households)], | |
| 'median_income':[float(median_income)], | |
| } | |
| #test_features = pd.DataFrame(data, columns=['longitude', 'latitude', 'housing_median_age', 'total_rooms', | |
| # 'total_bedrooms', 'population', 'households', 'median_income']) | |
| # Create DataFrame | |
| test_features = pd.DataFrame(data) | |
| #test_features = test_features.append(data,ignore_index=True) | |
| test_features = test_features.dropna(subset=["total_bedrooms"]) | |
| ## 3. scale the numeric features in test set. | |
| ## important note: do not apply fit function on the test set, using same scalar from training set | |
| test_features_normalized = scaler.transform(test_features) | |
| test_features_normalized | |
| pred = lin_reg.predict(test_features_normalized)[0] | |
| map_file = draw_map_customize(longitude,latitude, fig_id='test',fig_extension='png' ) | |
| return pred,map_file | |
| ### configure inputs/outputs | |
| set_longitude = gr.inputs.Slider(-124.350000, -114.310000, step=0.5, default=-120, label = 'Longitude') | |
| set_latitude = gr.inputs.Slider(32, 41, step=0.5, default=33, label = 'Latitude') | |
| set_housing_median_age = gr.inputs.Slider(1, 52, step=1, default=10, label = 'Housing_median_age (Year)') | |
| set_total_rooms = gr.inputs.Slider(1, 20, step=5, default=12, label = 'Total_rooms') | |
| set_total_bedrooms = gr.inputs.Slider(1, 10, step=5, default=9, label = 'Total_bedrooms') | |
| set_population = gr.inputs.Slider(3, 35682, step=5, default=10, label = 'Population') | |
| set_households = gr.inputs.Slider(1, 6082, step=5, default=10, label = 'Households') | |
| set_median_income = gr.inputs.Slider(0, 25000000, step=0.5, default=10, label = 'Median_income') | |
| set_label = gr.outputs.Textbox(label="Predicted Housing Prices") | |
| # define output as the single class text | |
| set_out_images = gr.outputs.Image(label="Visualize your location") | |
| ### configure gradio, detailed can be found at https://www.gradio.app/docs/#i_slider | |
| interface = gr.Interface(fn=predict_price, | |
| inputs=[set_longitude, set_latitude,set_housing_median_age,set_total_rooms,set_total_bedrooms,set_population,set_households,set_median_income], | |
| outputs=[set_label,set_out_images], | |
| examples_per_page = 2, | |
| examples = get_sample_data(10), | |
| title="Housing Price Prediction", | |
| description= "Click examples below for a quick demo", | |
| theme = 'huggingface', | |
| layout = 'vertical' | |
| ) | |
| interface.launch(debug=True) |