Spaces:
Sleeping
Sleeping
File size: 2,953 Bytes
d9f8b0b 31e1d52 4c09be8 d9f8b0b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
import gradio as gr
longitude = gr.inputs.Slider(-124.00, -114.00, step=0.01, label = "longitude")
latitude = gr.inputs.Slider(32.00, 42.00, step=0.01, label = "latitude")
housing_median_age = gr.inputs.Slider(1, 100, step=1, label = "housing_median_age")
total_rooms = gr.inputs.Slider(1, 50000, step=1, label = "total_rooms")
total_bedrooms = gr.inputs.Slider(1, 10000, step=1, label = "total_bedrooms")
population = gr.inputs.Slider(1, 40000, step=1, label = "population")
households = gr.inputs.Slider(1, 6100, step=1, label = "households")
median_income = gr.inputs.Slider(0.1, 50, step=0.15, label = "median_income")
pred_house_price = gr.outputs.Textbox(label = "predicting housing price")
from sklearn.model_selection import train_test_split
import pandas as pd
## 4. scale the numeric features in training set
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
import joblib
import os
def housing_predict(longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population,
households, median_income):
"""
[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population,
households, median_income]
"""
item = [longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population,
households, median_income]
print(f"passing parameters: {item}")
if os.path.exists("sample_data/LinearRegressionHousing.pkl"):
regr = joblib.load("sample_data/LinearRegressionHousing.pkl")
print("load directly from pkl file")
else:
print("load training data...")
housing = pd.read_csv('sample_data/housing.csv')
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
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)
lin_reg = LinearRegression() ## Initialize the class
regr = lin_reg.fit(train_features_normalized, train_labels) # feed the training data X, and label Y for supervised learning
if not os.path.exists("sample_data/LinearRegressionHousing.pkl"):
joblib.dump(regr, "sample_data/LinearRegressionHousing.pkl")
res = regr.predict([ item])
print(f"res={res}")
return str(res[0])
gr.Interface(fn=housing_predict, inputs=[longitude, latitude, housing_median_age, total_rooms, total_bedrooms, population,
households, median_income], outputs=pred_house_price).launch(debug=True)
|