Predict steel plate faults¶

!pip install datarobot umap-learn nbformat hdbscan

%matplotlib inline

import datarobot as dr
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd

light_blue = "#598fd6"
grey_blue = "#5f728b"
orange = "#dd6b3d"

# If the config file is not in the default location described in the API Quickstart guide, '~/.config/datarobot/drconfig.yaml', then you will need to call
# dr.Client(config_path='path-to-drconfig.yaml')

def sorted_by_metric(models, test_set, metric):
    models_with_score = [model for model in models if model.metrics[metric][test_set] is not None]

    return sorted(models_with_score, key=lambda model: model.metrics[metric][test_set])

# Set styling
dr_dark_blue = "#08233F"
dr_blue = "#1F77B4"
dr_orange = "#FF7F0E"
dr_red = "#BE3C28"

# Create function to build Historgrams


def rebin_df(raw_df, number_of_bins):
    cols = ["bin", "actual_mean", "predicted_mean", "bin_weight"]
    new_df = pd.DataFrame(columns=cols)
    current_prediction_total = 0
    current_actual_total = 0
    current_row_total = 0
    x_index = 1
    bin_size = 60 / number_of_bins
    for rowId, data in raw_df.iterrows():
        current_prediction_total += data["predicted"] * data["bin_weight"]
        current_actual_total += data["actual"] * data["bin_weight"]
        current_row_total += data["bin_weight"]

        if (rowId + 1) % bin_size == 0:
            x_index += 1
            bin_properties = {
                "bin": ((round(rowId + 1) / 60) * number_of_bins),
                "actual_mean": current_actual_total / current_row_total,
                "predicted_mean": current_prediction_total / current_row_total,
                "bin_weight": current_row_total,
            }

            new_df = new_df.append(bin_properties, ignore_index=True)
            current_prediction_total = 0
            current_actual_total = 0
            current_row_total = 0
    return new_df

# Create function to build lift charts


def matplotlib_lift(bins_df, bin_count, ax):
    grouped = rebin_df(bins_df, bin_count)
    ax.plot(
        range(1, len(grouped) + 1),
        grouped["predicted_mean"],
        marker="+",
        lw=1,
        color=dr_blue,
        label="predicted",
    )
    ax.plot(
        range(1, len(grouped) + 1),
        grouped["actual_mean"],
        marker="*",
        lw=1,
        color=dr_orange,
        label="actual",
    )
    ax.set_xlim([0, len(grouped) + 1])
    ax.set_facecolor(dr_dark_blue)
    ax.legend(loc="best")
    ax.set_title("Lift chart {} bins".format(bin_count))
    ax.set_xlabel("Sorted Prediction")
    ax.set_ylabel("Value")
    return grouped

dr_roc_green = "#03c75f"
white = "#ffffff"
dr_purple = "#65147D"
dr_dense_green = "#018f4f"

threshold = roc.get_best_f1_threshold()
fig = plt.figure(figsize=(8, 8))
axes = fig.add_subplot(1, 1, 1, facecolor=dr_dark_blue)

plt.scatter(roc_df.false_positive_rate, roc_df.true_positive_rate, color=dr_roc_green)
plt.plot(roc_df.false_positive_rate, roc_df.true_positive_rate, color=dr_roc_green)
plt.plot([0, 1], [0, 1], color=white, alpha=0.25)
plt.title("ROC curve")
plt.xlabel("False Positive Rate")
plt.xlim([0, 1])
plt.ylabel("True Positive Rate")
plt.ylim([0, 1])

deployment_id = "YOUR_DEPLOYMENT_ID"

dr.Credential.list()

# Scoring without Prediction Explanations
if False:
    dr.BatchPredictionJob.score(
        deployment_id,
        intake_settings={
            "type": "localFile",
            "file": "inputfile.csv",  # Provide the filepath, Pandas dataframe, or file-like object here
        },
        output_settings={"type": "localFile", "path": "outputfile.csv"},
    )

# Scoring with Prediction Explanations
if False:
    dr.BatchPredictionJob.score(
        deployment_id,
        intake_settings={
            "type": "localFile",
            "file": "inputfile.csv",  # Provide the filepath, Pandas dataframe, or file-like object here
        },
        output_settings={"type": "localFile", "path": "outputfile.csv"},
        max_explanations=3,  # Compute Prediction Explanations for the amount of features indicated here
    )

if False:
    dr.BatchPredictionJob.score(
        deployment_id,
        intake_settings={
            "type": "s3",
            "url": "s3://theos-test-bucket/lending_club_scoring.csv",  # Provide the URL of your datastore here
            "credential_id": "YOUR_CREDENTIAL_ID_FROM_ABOVE",  # Provide your credentials here
        },
        output_settings={
            "type": "s3",
            "url": "s3://theos-test-bucket/lending_club_scored2.csv",
            "credential_id": "YOUR_CREDENTIAL_ID_FROM_ABOVE",
        },
    )

if False:
    dr.BatchPredictionJob.score(
        deployment_id,
        intake_settings={
            "type": "jdbc",
            "table": "table_name",
            "schema": "public",
            "dataStoreId": data_store.id,  # Provide the ID of your datastore here
            "credentialId": cred.credential_id,  # Provide your credentials here
        },
        output_settings={
            "type": "jdbc",
            "table": "table_name",
            "schema": "public",
            "statementType": "insert",
            "dataStoreId": data_store.id,
            "credentialId": cred.credential_id,
        },
    )