| import pickle |
|
|
| import matplotlib.pyplot as plt |
| import pandas as pd |
| import seaborn as sns |
| import streamlit as st |
| from sklearn.ensemble import RandomForestClassifier |
| from sklearn.metrics import accuracy_score |
| from sklearn.model_selection import train_test_split |
|
|
| st.title("Penguin Classifier") |
|
|
| st.write( |
| """App uses 6 inputs to predict |
| the species of penguin using a model |
| built on the Palmer's Penguins dataset. |
| """ |
| ) |
|
|
| |
| password_guess = st.text_input("Input Password?") |
| if password_guess != "streamlit": |
| st.stop() |
|
|
| |
| penguin_file = st.file_uploader("Upload your own data") |
|
|
| if penguin_file is None: |
| rf_pickle = open("models/rf_penguin.pickle", "rb") |
| map_pickle = open("models/class_penguin.pickle", "rb") |
| rfc = pickle.load(rf_pickle) |
| unique_penguin_mapping = pickle.load(map_pickle) |
| rf_pickle.close() |
| map_pickle.close() |
| penguin_df = pd.read_csv("penguins.csv") |
| else: |
| penguin_df = pd.read_csv(penguin_file) |
| penguin_df['sex'].fillna(penguin_df['sex'].mode()[0], inplace=True) |
| penguin_df = penguin_df.dropna() |
| output = penguin_df["species"] |
| features = penguin_df[ |
| [ |
| "island", |
| "bill_length_mm", |
| "bill_depth_mm", |
| "flipper_length_mm", |
| "body_mass_g", |
| "sex", |
| ] |
| ] |
| features = pd.get_dummies(features) |
| output, unique_penguin_mapping = pd.factorize(output) |
| x_train, x_test, y_train, y_test = train_test_split(features, output, test_size=0.8) |
| rfc = RandomForestClassifier(random_state=15) |
| rfc.fit(x_train.values, y_train) |
| y_pred = rfc.predict(x_test.values) |
| score = round(accuracy_score(y_pred, y_test), 2) |
| |
| rf_pickle = open("models/rf_penguin.pickle", "wb") |
| pickle.dump(rfc, rf_pickle) |
| rf_pickle.close() |
|
|
| output_pickle = open("models/class_penguin.pickle", "wb") |
| pickle.dump(unique_penguin_mapping, output_pickle) |
| output_pickle.close() |
| st.write( |
| f"""Trained a Random Forest model on these data, |
| it has a score of {score}! """ |
| ) |
|
|
| |
| with st.form("user_inputs"): |
| island = st.selectbox("Penguin Island", options=["Biscoe", "Dream", "Torgerson"]) |
| sex = st.selectbox("Sex", options=["Female", "Male"]) |
| bill_length = st.number_input("Bill Length (mm)", min_value=0) |
| bill_depth = st.number_input("Bill Depth (mm)", min_value=0) |
| flipper_length = st.number_input("Flipper Length (mm)", min_value=0) |
| body_mass = st.number_input("Body Mass (g)", min_value=0) |
| st.form_submit_button() |
|
|
| island_biscoe, island_dream, island_torgerson = 0, 0, 0 |
| if island == "Biscoe": |
| island_biscoe = 1 |
| elif island == "Dream": |
| island_dream = 1 |
| elif island == "Torgerson": |
| island_torgerson = 1 |
|
|
| sex_female, sex_male = 0, 0 |
| if sex == "Female": |
| sex_female = 1 |
| elif sex == "Male": |
| sex_male = 1 |
|
|
| |
| new_prediction = rfc.predict( |
| [ |
| [ |
| bill_length, |
| bill_depth, |
| flipper_length, |
| body_mass, |
| island_biscoe, |
| island_dream, |
| island_torgerson, |
| sex_female, |
| sex_male, |
| ] |
| ] |
| ) |
| st.subheader("Predicting Your Penguin's Species:") |
| prediction_species = unique_penguin_mapping[new_prediction][0] |
| st.write(f"# Prediction Species: **{prediction_species}") |
| st.write( |
| """Machine learning |
| (Random Forest) model to predict the |
| species, the features used in this |
| prediction are ranked by relative |
| importance below.""" |
| ) |
|
|
| st.write( |
| """Below are the histograms for each |
| continuous variable separated by penguin species. |
| The vertical line represents the inputted value.""" |
| ) |
|
|
| fig, ax = plt.subplots() |
| ax = sns.displot(x=penguin_df["bill_length_mm"], hue=penguin_df["species"]) |
| plt.axvline(bill_length) |
| plt.title("Bill Length by Species") |
| st.pyplot(ax) |
|
|
| fig, ax = plt.subplots() |
| ax = sns.displot(x=penguin_df["bill_depth_mm"], hue=penguin_df["species"]) |
| plt.axvline(bill_depth) |
| plt.title("Bill Depth by Species") |
| st.pyplot(ax) |
|
|
| fig, ax = plt.subplots() |
| ax = sns.displot(x=penguin_df["flipper_length_mm"], hue=penguin_df["species"]) |
| plt.axvline(flipper_length) |
| plt.title("Flipper Length by Species") |
| st.pyplot(ax) |
|
|
