API Reference

scrybe.bookmark(obj, obj_name: str, msg: str)

Bookmark an object using this API and then filter in the dashboard by clicking the “Bookmarked” button

Parameters:

• obj – The object to be bookmarked. This can be a Scrybe tracked model, numpy.ndarray or pandas dataframe or series, or a matplotlib figure object
• obj_name – The name of the object which will be displayed in the Scrybe dashboard
• msg – The description of why the bookmarked object is important

Returns:

None

>>> for model_pipe in models_pipe:
>>>     model_pipe.fit(train, y)
>>>     preds = model_pipe.predict(test)
>>>     if round(np.sqrt(mean_squared_error(y_test, preds)), 2) <= 0.2:
>>>         scrybe.bookmark(obj=model_pipe, obj_name="pipeline_model", msg="Shortlisted models with RMSE <= 0.2")

scrybe.log_code_files(filepaths: list)

Upload the code of files. Need not call this for IPython. IPython code is automatically captured when .fit is called on the model object. Code capture for vanilla python interpreter is currently not supported.

Parameters:filepaths – List of paths of the files that needs to be uploaded. Files with extension .ipynb notebook will be ignored Returns:None

>>> scrybe.log_code_files(filepaths=["models/model_architecture.py", "metrics/metric_funcs.py"])

scrybe.log_custom_data_statistic(data, stats_name: str, stats_value: Union[int, float, str])

Log custom dataset statistics on a dataset. CAUTION: This must be called AFTER model.fit

Parameters:

• data – Must be one of Pandas Dataframe/Series, Numpy array, Spark dataframe/rdd
• stats_name – Name of the custom stats. Must be a string
• stats_value – Value of the custom stats. Must str, int or float

Returns:

None

>>> dataset = pandas.read_csv(url, names=names)
>>> num_frauds = get_fraud_count(dataset)
>>> scrybe.log_custom_data_statistic(data=dataset, stats_name="num_fraud", stats_value=num_frauds)

scrybe.log_custom_hyperparameter(model, param_name: str, param_value)

Log a hyperparameter of a model. CAUTION: This must be called BEFORE model.fit

Parameters:

• model – The estimator object on which fit can be called.
• param_name – Name of the custom hyperparameter. Must be a string
• param_value – Value of the custom hyperparameter. Must be a JSON Serializable object

Returns:

None

>>> knn = KNeighborsClassifier()
>>> scrybe.log_custom_hyperparameter(knn, param_name='custom_param', param_value=float('nan'))
>>> grid_search = GridSearchCV(knn, param_grid=knn_params, cv=2, scoring='accuracy', n_jobs=1)
>>> grid_search.fit(x_train, y_train)

scrybe.log_custom_model_evaluation_metric(model, x_test, y_test, param_name: str, param_value: Union[int, float, str])

Log custom metric of a model. CAUTION: This must be called AFTER model.fit

Parameters:

• model – The trained model object which can be used to make predictions or the model id displayed on scrybe dashboard
• x_test – The test samples dataset. Must be one of Pandas Dataframe/Series, Numpy array, Spark dataframe/rdd
• y_test – The test labels dataset. Must be one of Pandas Dataframe/Series, Numpy array, Spark dataframe/rdd
• param_name – Name of the custom metric. Must be a string
• param_value – Value of the custom metric. Must str, int or float

Returns:

None

>>> model = keras.model.load_model("convnet_mnist.h5")
>>> probabilities = model.predict(x_test)
>>> probabilities[probabilities > 0.3] = 2]
>>> probabilities[probabilities < 0.7] = 2]
>>> probabilities[probabilities < 0.3] = 0]
>>> probabilities[probabilities > 0.7] = 1]
>>> cannot_classify = get_unclassified_images(probabilities, y_test)
>>> scrybe.log_custom_model_evaluation_metric(model=model, x_test=x_test, y_test=y_test,
...                                           param_name="unclassified_images", param_value=cannot_classify)

scrybe.log_feature_importances(model, feature_importances: dict)

Log variable importance of a model. CAUTION: This must be called AFTER model.fit

Parameters:

• model – The trained model object which can be used to make predictions
• feature_importances – A dictionary of features as keys and the corresponding importance as value

Returns:

None

>>> knn = KNeighborsClassifier()
>>> knn.fit(x_train, y_train)
>>> scrybe.log_feature_importances(model=knn, feature_importances={'sepal-length': 0.9, 'sepal-width': 0.6,
...                                                                'petal-length': 0.4, 'petal-width': 0.1})

scrybe.log_features(model, feature_names: list)

Log feature names of a model. Feature names should be ordered in the same way in which they were fed to the model. CAUTION: This must be called AFTER model.fit

Parameters:

• model – The trained model object which can be used to make predictions
• feature_names – A list of string containing all the feature names

Returns:

None

>>> rf = RandomForestRegressor(featuresCol="features", labelCol="item_cnt_month")
>>> rf_model = rf.fit(transformed)
>>> scrybe.log_features(model=rf_model, feature_names=['shop_id', 'item_id', 'item_cnt', 'transactions',
...                                    'year', 'item_cnt_mean', 'item_cnt_std'])

scrybe.peek(obj)

Use this API to directly visit the web page with details of a given object in the dashboard

Parameters:obj – Can be a model or a dataset or figure object Returns:Prints URL to stdout. If executed from notebook then loads the URL

>>> import pandas
>>> import matplotlib.pyplot as plt
>>> import seaborn as sns
>>> url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/iris.csv"
>>> names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
>>> dataset = pandas.read_csv(url, names=names)
>>> sns.boxplot('sepal-length', 'sepal-width', data=dataset)
>>> scrybe.peek(plt.gcf())

scrybe.set_label(label: Union[str, list, None])

Set labels/tags on all the models, datasets and plots which helps you in filtering data in the dashboard

Parameters:label – Label can be string or a list of string with which you can filter in the dashboard Returns:None

>>> dataset = spark.read.csv("s3a://test-datasets/")
>>> android_dataset = dataset.filter('os == "android"')
>>> scrybe.set_label(label="android")
>>> rf = RandomForestRegressor(featuresCol="features", labelCol="ltv")
>>> rf_model = rf.fit(android_dataset)  ## The model gets tagged as "android"
>>> ios_dataset = dataset.filter('os == "ios" and region == "US"')
>>> scrybe.set_label(label=["ios", "US"])
>>> rf = RandomForestRegressor(featuresCol="features", labelCol="ltv")
>>> rf_model = rf.fit(ios_dataset)  ## The model gets tagged as "ios" and "US"