Local Resources

All the resources in BigML can be downloaded and used locally with no connection whatsoever to BigML’s servers. This is specially important for all Supervised and Unsupervised models, that can be used to generate predictions in any programmable device. The next sections describe how to do that for each type of resource, but as a general rule, resources can be exported to a JSON file in your file system using the export method.

api.export('model/5143a51a37203f2cf7000956',
           'filename': 'my_dir/my_model.json')

The contents of the generated file can be used just as the remote model to generate predictions. As you’ll see in next section, the local Model object can be instantiated by giving the path to this file as first argument:

from bigml.model import Model
local_model = Model("my_dir/my_model.json")
local_model.predict({"petal length": 3, "petal width": 1})
Iris-versicolor

These bindings define a particular class for each type of Machine Learning model that is able to interpret the corresponding JSON and create the local predictions. The classes can be instantiated using:

• The ID of the resource: In this case, the class looks for the JSON information of the resource first locally (expecting to find a file in the local storage directory –./storage by default – whose name is the ID of the model after replacing / by _) and also remotely if absent.

from bigml.model import Model
from bigml.api import BigML

local_model = Model('model/502fdbff15526876610002615')

• A dictionary containing the resource information. In this case, the class checks that this information belongs to a finished resource and contains the attributes needed to create predictions, like the fields structure. If any of these attributes is absent, retrieves the ID of the model and tries to download the correct JSON from the API to store it locally for further use.

from bigml.anomaly import Anomaly
from bigml.api import BigML
api = BigML()
anomaly = api.get_anomaly('anomaly/502fdbff15526876610002615',
                          query_string='only_model=true;limit=-1')

local_anomaly = Anomaly(anomaly)

• A path to the file that contains the JSON information for the resource. In this case, the file is read and the same checks mentioned above are done. If any of these checks fails, it tries to retrieve the correct JSON from the API to store it locally for further use.

from bigml.logistic import LogisticRegression
local_logistic_regression = LogisticRegression('./my_logistic.json')

Internally, these classes need a connection object (api = BigML()) to:

• Know the local storage in your file system.
• Download the JSON of the resource if the information provided is not the full finished resource content.

Users can provide the connection as a second argument when instantiating the class:

from bigml.cluster import Cluster
from bigml.api import BigML

local_cluster = Cluster('cluster/502fdbff15526876610002435',
                        api=BigML(my_username,
                                  my_api_key
                                  storage="my_storage"))

If no connection is provided, a default connection will be instantiated internally. This default connection will use ./storage as default storage directory and the credentials used to connect to the API when needed are retrieved from the BIGML_USERNAME and BIGML_API_KEY environment variables. If no credentials are found in your environment, any attempt to download the information will raise a condition asking the user to set these variables.

Ensembles and composite objects, like Fusions, need more than one resource to be downloaded and stored locally for the class to work. In this case, the class needs all the component models, so providing only a local file or a dictionary containing the JSON for the resource is not enough for the Ensemble or Fusion objects to be fully instantiated. If you only provide that partial information, the class will use the internal API connection the first time to download the components. However, using the api.export method for ensembles or fusions will download these component models for you and will store them in the same directory as the file used to store the ensemble or fusion information. After that, you can instantiate the object using the path to the file where the ensemble or fusion information was stored. The class will look internally for the rest of components in the same directory and find them, so no connection to the API will be done.

If you use a tag to label the resource, you can also ask for the last resource that has the tag:

api.export_last('my_tag',
                resource_type='ensemble',
                'filename': 'my_dir/my_ensemble.json')

and even for a resource inside a project:

api.export_last('my_tag',
                resource_type='dataset',
                project='project/5143a51a37203f2cf7000959',
                'filename': 'my_dir/my_dataset.json')

Local Models

You can instantiate a local version of a remote model.

from bigml.model import Model
local_model = Model('model/502fdbff15526876610002615')

This will retrieve the remote model information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a Model object that will be stored in the ./storage directory and you can use to make local predictions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.model import Model
from bigml.api import BigML

local_model = Model('model/502fdbff15526876610002615',
                    api=BigML(my_username,
                              my_api_key,
                              storage="my_storage"))

or even use the remote model information previously retrieved to build the local model object:

from bigml.model import Model
from bigml.api import BigML
api = BigML()
model = api.get_model('model/502fdbff15526876610002615',
                      query_string='only_model=true;limit=-1')

local_model = Model(model)

As you can see, the query_string used to retrieve the model has two parts. They both act on the fields information that is added to the JSON response. First only_model=true is used to restrict the fields described in the fields structure of the response to those used as predictors in the model. Also limit=-1 avoids the pagination of fields which is used by default and includes them all at once. These details are already taken care of in the two previous examples, where the model ID is used as argument.

Any of these methods will return a Model object that you can use to make local predictions, generate IF-THEN rules, Tableau rules or a Python function that implements the model.

You can also build a local model from a model previously retrieved and stored in a JSON file:

from bigml.model import Model
local_model = Model('./my_model.json')

Local Predictions

Once you have a local model you can use to generate predictions locally.

local_model.predict({"petal length": 3, "petal width": 1})
Iris-versicolor

Local predictions have three clear advantages:

• Removing the dependency from BigML to make new predictions.
• No cost (i.e., you do not spend BigML credits).
• Extremely low latency to generate predictions for huge volumes of data.

The default output for local predictions is the prediction itself, but you can also add other properties associated to the prediction, like its confidence or probability, the distribution of values in the predicted node (for decision tree models), and the number of instances supporting the prediction. To obtain a dictionary with the prediction and the available additional properties use the full=True argument:

local_model.predict({"petal length": 3, "petal width": 1}, full=True)

that will return:

{'count': 47,
 'confidence': 0.92444,
 'probability': 0.9861111111111112,
 'prediction': u'Iris-versicolor',
 'distribution_unit': 'categories',
 'path': [u'petal length > 2.45',
          u'petal width <= 1.75',
          u'petal length <= 4.95',
          u'petal width <= 1.65'],
 'distribution': [[u'Iris-versicolor', 47]]}

Note that the path attribute for the proportional missing strategy shows the path leading to a final unique node, that gives the prediction, or to the first split where a missing value is found. Other optional attributes are next which contains the field that determines the next split after the prediction node and distribution that adds the distribution that leads to the prediction. For regression models, min and max will add the limit values for the data that supports the prediction.

When your test data has missing values, you can choose between last prediction or proportional strategy to compute the prediction. The last prediction strategy is the one used by default. To compute a prediction, the algorithm goes down the model’s decision tree and checks the condition it finds at each node (e.g.: ‘sepal length’ > 2). If the field checked is missing in your input data you have two options: by default (last prediction strategy) the algorithm will stop and issue the last prediction it computed in the previous node. If you chose proportional strategy instead, the algorithm will continue to go down the tree considering both branches from that node on. Thus, it will store a list of possible predictions from then on, one per valid node. In this case, the final prediction will be the majority (for categorical models) or the average (for regressions) of values predicted by the list of predicted values.

You can set this strategy by using the missing_strategy argument with code 0 to use last prediction and 1 for proportional.

from bigml.model import LAST_PREDICTION, PROPORTIONAL
# LAST_PREDICTION = 0; PROPORTIONAL = 1
local_model.predict({"petal length": 3, "petal width": 1},
                    missing_strategy=PROPORTIONAL)

For classification models, it is sometimes useful to obtain a probability or confidence prediction for each possible class of the objective field. To do this, you can use the predict_probability and predict_confidence methods respectively. The former gives a prediction based on the distribution of instances at the appropriate leaf node, with a Laplace correction based on the root node distribution. The latter returns a lower confidence bound on the leaf node probability based on the Wilson score interval.

Each of these methods take the missing_strategy argument that functions as it does in predict, and one additional argument, compact. If compact is False (the default), the output of these functions is a list of maps, each with the keys prediction and probability (or confidence) mapped to the class name and its associated probability (or confidence). Note that these methods substitute the deprecated multiple parameter in the predict method functionallity.

So, for example, the following:

local_model.predict_probability({"petal length": 3})

would result in

[{'prediction': u'Iris-setosa',
  'probability': 0.0033003300330033},
 {'prediction': u'Iris-versicolor',
  'probability': 0.4983498349834984},
 {'prediction': u'Iris-virginica',
  'probability': 0.4983498349834984}]

If compact is True, only the probabilities themselves are returned, as a list in class name order. Note that, for reference, the attribute Model.class_names contains the class names in the appropriate ordering.

To illustrate, the following:

local_model.predict_probability({"petal length": 3}, compact=True)

would result in

[0.0033003300330033, 0.4983498349834984, 0.4983498349834984]

The output of predict_confidence is the same, except that the output maps are keyed with confidence instead of probability.

For classifications, the prediction of a local model will be one of the available categories in the objective field and an associated confidence or probability that is used to decide which is the predicted category. If you prefer the model predictions to be operated using any of them, you can use the operating_kind argument in the predict method. Here’s the example to use predictions based on confidence:

local_model.predict({"petal length": 3, "petal width": 1},
                    {"operating_kind": "confidence"})

Previous versions of the bindings had additional arguments in the predict method that were used to format the prediction attributes. The signature of the method has been changed to accept only arguments that affect the prediction itself, (like missing_strategy, operating_kind and opreating_point) and full which is a boolean that controls whether the output is the prediction itself or a dictionary will all the available properties associated to the prediction. Formatting can be achieved by using the cast_prediction function:

def cast_prediction(full_prediction, to=None,
                    confidence=False, probability=False,
                    path=False, distribution=False,
                    count=False, next=False, d_min=False,
                    d_max=False, median=False,
                    unused_fields=False):

whose first argument is the prediction obtained with the full=True argument, the second one defines the type of output (None to obtain the prediction output only, “list” or “dict”) and the rest of booleans cause the corresponding property to be included or not.

Operating point’s predictions

In classification problems, Models, Ensembles and Logistic Regressions can be used at different operating points, that is, associated to particular thresholds. Each operating point is then defined by the kind of property you use as threshold, its value and a the class that is supposed to be predicted if the threshold is reached.

Let’s assume you decide that you have a binary problem, with classes True and False as possible outcomes. Imagine you want to be very sure to predict the True outcome, so you don’t want to predict that unless the probability associated to it is over 0,8. You can achieve this with any classification model by creating an operating point:

operating_point = {"kind": "probability",
                   "positive_class": "True",
                   "threshold": 0.8};

to predict using this restriction, you can use the operating_point parameter:

prediction = local_model.predict(inputData,
                                 operating_point=operating_point)

where inputData should contain the values for which you want to predict. Local models allow two kinds of operating points: probability and confidence. For both of them, the threshold can be set to any number in the [0, 1] range.

Local Clusters

You can also instantiate a local version of a remote cluster.

from bigml.cluster import Cluster
local_cluster = Cluster('cluster/502fdbff15526876610002435')

This will retrieve the remote cluster information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a Cluster object that will be stored in the ./storage directory and you can use to make local centroid predictions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.cluster import Cluster
from bigml.api import BigML

local_cluster = Cluster('cluster/502fdbff15526876610002435',
                        api=BigML(my_username,
                                  my_api_key
                                  storage="my_storage"))

or even use the remote cluster information previously retrieved to build the local cluster object:

from bigml.cluster import Cluster
from bigml.api import BigML
api = BigML()
cluster = api.get_cluster('cluster/502fdbff15526876610002435',
                          query_string='limit=-1')

local_cluster = Cluster(cluster)

Note that in this example we used a limit=-1 query string for the cluster retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local clusters provide also methods for the significant operations that can be done using clusters: finding the centroid assigned to a certain data point, sorting centroids according to their distance to a data point, summarizing the centroids intra-distances and inter-distances and also finding the closest points to a given one. The Local Centroids and the Summary generation sections will explain these methods.

Local Centroids

Using the local cluster object, you can predict the centroid associated to an input data set:

local_cluster.centroid({"pregnancies": 0, "plasma glucose": 118,
                        "blood pressure": 84, "triceps skin thickness": 47,
                        "insulin": 230, "bmi": 45.8,
                        "diabetes pedigree": 0.551, "age": 31,
                        "diabetes": "true"})
{'distance': 0.454110207355, 'centroid_name': 'Cluster 4',
 'centroid_id': '000004'}

You must keep in mind, though, that to obtain a centroid prediction, input data must have values for all the numeric fields. No missing values for the numeric fields are allowed unless you provided a default_numeric_value in the cluster construction configuration. If so, this value will be used to fill the missing numeric fields.

As in the local model predictions, producing local centroids can be done independently of BigML servers, so no cost or connection latencies are involved.

Another interesting method in the cluster object is local_cluster.closests_in_cluster, which given a reference data point will provide the rest of points that fall into the same cluster sorted in an ascending order according to their distance to this point. You can limit the maximum number of points returned by setting the number_of_points argument to any positive integer.

local_cluster.closests_in_cluster( \
    {"pregnancies": 0, "plasma glucose": 118,
     "blood pressure": 84, "triceps skin thickness": 47,
     "insulin": 230, "bmi": 45.8,
     "diabetes pedigree": 0.551, "age": 31,
     "diabetes": "true"}, number_of_points=2)

The response will be a dictionary with the centroid id of the cluster an the list of closest points and their distances to the reference point.

{'closest': [ \
    {'distance': 0.06912270988567025,
     'data': {'plasma glucose': '115', 'blood pressure': '70',
              'triceps skin thickness': '30', 'pregnancies': '1',
              'bmi': '34.6', 'diabetes pedigree': '0.529',
              'insulin': '96', 'age': '32', 'diabetes': 'true'}},
    {'distance': 0.10396456577958413,
     'data': {'plasma glucose': '167', 'blood pressure': '74',
     'triceps skin thickness': '17', 'pregnancies': '1', 'bmi': '23.4',
     'diabetes pedigree': '0.447', 'insulin': '144', 'age': '33',
     'diabetes': 'true'}}],
'reference': {'age': 31, 'bmi': 45.8, 'plasma glucose': 118,
              'insulin': 230, 'blood pressure': 84,
              'pregnancies': 0, 'triceps skin thickness': 47,
              'diabetes pedigree': 0.551, 'diabetes': 'true'},
'centroid_id': u'000000'}

No missing numeric values are allowed either in the reference data point. If you want the data points to belong to a different cluster, you can provide the centroid_id for the cluster as an additional argument.

Other utility methods are local_cluster.sorted_centroids which given a reference data point will provide the list of centroids sorted according to the distance to it

local_cluster.sorted_centroids( \
{'plasma glucose': '115', 'blood pressure': '70',
 'triceps skin thickness': '30', 'pregnancies': '1',
 'bmi': '34.6', 'diabetes pedigree': '0.529',
 'insulin': '96', 'age': '32', 'diabetes': 'true'})
{'centroids': [{'distance': 0.31656890408929705,
                'data': {u'000006': 0.34571, u'000007': 30.7619,
                         u'000000': 3.79592, u'000008': u'false'},
                'centroid_id': u'000000'},
               {'distance': 0.4424198506958207,
                'data': {u'000006': 0.77087, u'000007': 45.50943,
                         u'000000': 5.90566, u'000008': u'true'},
                'centroid_id': u'000001'}],
 'reference': {'age': '32', 'bmi': '34.6', 'plasma glucose': '115',
               'insulin': '96', 'blood pressure': '70',
               'pregnancies': '1', 'triceps skin thickness': '30',
               'diabetes pedigree': '0.529', 'diabetes': 'true'}}

or points_in_cluster that returns the list of data points assigned to a certain cluster, given its centroid_id.

centroid_id = "000000"
local_cluster.points_in_cluster(centroid_id)

Local Anomaly Detector

You can also instantiate a local version of a remote anomaly.

from bigml.anomaly import Anomaly
local_anomaly = Anomaly('anomaly/502fcbff15526876610002435')

This will retrieve the remote anomaly detector information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return an Anomaly object that will be stored in the ./storage directory and you can use to make local anomaly scores. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.anomaly import Anomaly
from bigml.api import BigML

local_anomaly = Anomaly('anomaly/502fcbff15526876610002435',
                        api=BigML(my_username,
                                  my_api_key,
                                  storage="my_storage_dir"))

or even use the remote anomaly information retrieved previously to build the local anomaly detector object:

from bigml.anomaly import Anomaly
from bigml.api import BigML
api = BigML()
anomaly = api.get_anomaly('anomaly/502fcbff15526876610002435',
                          query_string='limit=-1')

local_anomaly = Anomaly(anomaly)

Note that in this example we used a limit=-1 query string for the anomaly retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

The anomaly detector object has also the method anomalies_filter that will build the LISP filter you would need to filter the original dataset and create a new one excluding the top anomalies. Setting the include parameter to True you can do the inverse and create a dataset with only the most anomalous data points.

Local Anomaly Scores

Using the local anomaly detector object, you can predict the anomaly score associated to an input data set:

local_anomaly.anomaly_score({"src_bytes": 350})
0.9268527808726705

As in the local model predictions, producing local anomaly scores can be done independently of BigML servers, so no cost or connection latencies are involved.

Local Anomaly caching

Anomalies can become quite large objects. That’s why their use of memory resources can be heavy. If your usual scenario is using many of them constantly in a disordered way, the best strategy is setting up a cache system to store them. The local anomaly class provides helpers to interact with that cache. Here’s an example using Redis.

from anomaly import Anomaly
import redis
r = redis.Redis()
# First build as you would any core Anomaly object:
anomaly = Anomaly('anomaly/5126965515526876630001b2')
# Store a serialized version in Redis
anomaly.dump(cache_set=r.set)
# (retrieve the external rep from its convenient place)
# Speedy Build from external rep
anomaly = Anomaly('anomaly/5126965515526876630001b2', cache_get=r.get)
# Get scores same as always:
anomaly.anomaly_score({"src_bytes": 350})

Local Logistic Regression

You can also instantiate a local version of a remote logistic regression.

from bigml.logistic import LogisticRegression
local_log_regression = LogisticRegression(
    'logisticregression/502fdbff15526876610042435')

This will retrieve the remote logistic regression information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a LogisticRegression object that will be stored in the ./storage directory and you can use to make local predictions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.logistic import LogisticRegression
from bigml.api import BigML

local_log_regression = LogisticRegression(
    'logisticregression/502fdbff15526876610602435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote logistic regression JSON structure as previously retrieved to build the local logistic regression object:

from bigml.logistic import LogisticRegression
from bigml.api import BigML
api = BigML()
logistic_regression = api.get_logistic_regression(
    'logisticregression/502fdbff15526876610002435',
    query_string='limit=-1')

local_log_regression = LogisticRegression(logistic_regression)

Note that in this example we used a limit=-1 query string for the logistic regression retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Logistic Regression Predictions

Using the local logistic regression object, you can predict the prediction for an input data set:

local_log_regression.predict({"petal length": 2, "sepal length": 1.5,
                              "petal width": 0.5, "sepal width": 0.7},
                              full=True)
{'distribution': [
    {'category': u'Iris-virginica', 'probability': 0.5041444478857267},
    {'category': u'Iris-versicolor', 'probability': 0.46926542042788333},
    {'category': u'Iris-setosa', 'probability': 0.02659013168639014}],
    'prediction': u'Iris-virginica', 'probability': 0.5041444478857267}

As you can see, the prediction contains the predicted category and the associated probability. It also shows the distribution of probabilities for all the possible categories in the objective field. If you only need the predicted value, you can remove the full argument.

You must keep in mind, though, that to obtain a logistic regression prediction, input data must have values for all the numeric fields. No missing values for the numeric fields are allowed.

For consistency of interface with the Model class, logistic regressions again have a predict_probability method, which takes the same argument as Model.predict: compact. As stated above, missing values are not allowed, and so there is no missing_strategy argument.

As with local Models, if compact is False (the default), the output is a list of maps, each with the keys prediction and probability mapped to the class name and its associated probability.

So, for example

local_log_regression.predict_probability({"petal length": 2, "sepal length": 1.5,
                                          "petal width": 0.5, "sepal width": 0.7})

[{'category': u'Iris-setosa', 'probability': 0.02659013168639014},
 {'category': u'Iris-versicolor', 'probability': 0.46926542042788333},
 {'category': u'Iris-virginica', 'probability': 0.5041444478857267}]

If compact is True, only the probabilities themselves are returned, as a list in class name order, again, as is the case with local Models.

Operating point predictions are also available for local logistic regressions and an example of it would be:

operating_point = {"kind": "probability",
                   "positive_class": "True",
                   "threshold": 0.8}
local_logistic.predict(inputData, operating_point=operating_point)

You can check the Operating point’s predictions section to learn about operating points. For logistic regressions, the only available kind is probability, that sets the threshold of probability to be reached for the prediction to be the positive class.

Local Logistic Regression

You can also instantiate a local version of a remote logistic regression:

from bigml.logistic import LogisticRegression
local_log_regression = LogisticRegression(
    'logisticregression/502fdbff15526876610042435')

This will retrieve the remote logistic regression information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a LogisticRegression object that will be stored in the ./storage directory and you can use to make local predictions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.logistic import LogisticRegression
from bigml.api import BigML

local_log_regression = LogisticRegression(
    'logisticregression/502fdbff15526876610602435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote logistic regression JSON structure as previously retrieved to build the local logistic regression object:

from bigml.logistic import LogisticRegression
from bigml.api import BigML
api = BigML()
logistic_regression = api.get_logistic_regression(
    'logisticregression/502fdbff15526876610002435',
    query_string='limit=-1')

local_log_regression = LogisticRegression(logistic_regression)

Note that in this example we used a limit=-1 query string for the logistic regression retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Linear Regression Predictions

Using the local LinearRegression class, you can predict the prediction for an input data set:

local_linear_regression.predict({"petal length": 2, "sepal length": 1.5,
                                 "species": "Iris-setosa",
                                 "sepal width": 0.7},
                                 full=True)
{'confidence_bounds': {
    'prediction_interval': 0.43783924497784293,
    'confidence_interval': 0.2561542783257394},
 'prediction': -0.6109005499999999, 'unused_fields': ['petal length']}

To obtain a linear regression prediction, input data can only have missing values for fields that had already some missings in training data.

The full=True in the predict method will cause the prediction to include confidence bounds when available. Some logistic regressions will not contain such information by construction. Also, in order to compute these bounds locally, you will need numpy and scipy in place. As they are quite heavy libraries, they aren’t automatically installed as dependencies of these bindings.

Local Deepnet

You can also instantiate a local version of a remote Deepnet.

from bigml.deepnet import Deepnet
local_deepnet = Deepnet(
    'deepnet/502fdbff15526876610022435')

This will retrieve the remote deepnet information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a Deepnet object that will be stored in the ./storage directory and you can use to make local predictions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.deepnet import Deepnet
from bigml.api import BigML

local_deepnet = Deepnet(
    'deepnet/502fdbff15526876610602435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote Deepnet JSON structure as previously retrieved to build the local Deepnet object:

from bigml.deepnet import Deepnet
from bigml.api import BigML
api = BigML()
deepnet = api.get_deepnet(
    'deepnet/502fdbff15526876610002435',
    query_string='limit=-1')

local_deepnet = Deepnet(deepnet)

Note that in this example we used a limit=-1 query string for the deepnet retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Deepnet Predictions

Using the local deepnet object, you can predict the prediction for an input data set:

local_deepnet.predict({"petal length": 2, "sepal length": 1.5,
                       "petal width": 0.5, "sepal width": 0.7},
                       full=True)
{'distribution': [
    {'category': u'Iris-virginica', 'probability': 0.5041444478857267},
    {'category': u'Iris-versicolor', 'probability': 0.46926542042788333},
    {'category': u'Iris-setosa', 'probability': 0.02659013168639014}],
    'prediction': u'Iris-virginica', 'probability': 0.5041444478857267}

As you can see, the full prediction contains the predicted category and the associated probability. It also shows the distribution of probabilities for all the possible categories in the objective field. If you only need the predicted value, you can remove the full argument.

To be consistent with the Model class interface, deepnets have also a predict_probability method, which takes the same argument as Model.predict: compact.

As with local Models, if compact is False (the default), the output is a list of maps, each with the keys prediction and probability mapped to the class name and its associated probability.

So, for example

local_deepnet.predict_probability({"petal length": 2, "sepal length": 1.5,
                                   "petal width": 0.5, "sepal width": 0.7})

[{'category': u'Iris-setosa', 'probability': 0.02659013168639014},
 {'category': u'Iris-versicolor', 'probability': 0.46926542042788333},
 {'category': u'Iris-virginica', 'probability': 0.5041444478857267}]

If compact is True, only the probabilities themselves are returned, as a list in class name order, again, as is the case with local Models.

Operating point predictions are also available for local deepnets and an example of it would be:

operating_point = {"kind": "probability",
                   "positive_class": "True",
                   "threshold": 0.8};
prediction = local_deepnet.predict(inputData,
                                   operating_point=operating_point)

Local Fusion

You can also instantiate a local version of a remote Fusion.

from bigml.fusion import Fusion
local_fusion = Fusion(
    'fusion/502fdbff15526876610022438')

This will retrieve the remote fusion information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a Fusion object that will be stored in the ./storage directory and you can use to make local predictions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.fusion import Fusion
from bigml.api import BigML

local_fusion = Fusion(
    'fusion/502fdbff15526876610602435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote Fusion JSON structure as previously retrieved to build the local Fusion object:

from bigml.fusion import Fusion
from bigml.api import BigML
api = BigML()
fusion = api.get_fusion(
    'fusion/502fdbff15526876610002435',
    query_string='limit=-1')

local_fusion = Fusion(fusion)

Note that in this example we used a limit=-1 query string for the fusion retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Fusion Predictions

Using the local fusion object, you can predict the prediction for an input data set:

local_fusion.predict({"petal length": 2, "sepal length": 1.5,
                      "petal width": 0.5, "sepal width": 0.7},
                      full=True)
{'prediction': u'Iris-setosa', 'probability': 0.45224}

As you can see, the full prediction contains the predicted category and the associated probability. If you only need the predicted value, you can remove the full argument.

To be consistent with the Model class interface, fusions have also a predict_probability method, which takes the same argument as Model.predict: compact.

As with local Models, if compact is False (the default), the output is a list of maps, each with the keys prediction and probability mapped to the class name and its associated probability.

So, for example

local_fusion.predict_probability({"petal length": 2, "sepal length": 1.5,
                                  "petal width": 0.5, "sepal width": 0.7})

[{'category': u'Iris-setosa', 'probability': 0.45224},
 {'category': u'Iris-versicolor', 'probability': 0.2854},
 {'category': u'Iris-virginica', 'probability': 0.26236}]

If compact is True, only the probabilities themselves are returned, as a list in class name order, again, as is the case with local Models.

Operating point predictions are also available with probability as threshold for local fusions and an example of it would be:

operating_point = {"kind": "probability",
                   "positive_class": "True",
                   "threshold": 0.8};
prediction = local_fusion.predict(inputData,
                                  operating_point=operating_point)

Local Association

You can also instantiate a local version of a remote association resource.

from bigml.association import Association
local_association = Association('association/502fdcff15526876610002435')

This will retrieve the remote association information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return an Association object that will be stored in the ./storage directory and you can use to extract the rules found in the original dataset. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.association import Association
from bigml.api import BigML

local_association = Association('association/502fdcff15526876610002435',
                                api=BigML(my_username,
                                          my_api_key
                                          storage="my_storage"))

or even use the remote association information retrieved previously to build the local association object:

from bigml.association import Association
from bigml.api import BigML
api = BigML()
association = api.get_association('association/502fdcff15526876610002435',
                                  query_string='limit=-1')

local_association = Association(association)

Note that in this example we used a limit=-1 query string for the association retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

The created Association object has some methods to help retrieving the association rules found in the original data. The get_rules method will return the association rules. Arguments can be set to filter the rules returned according to its leverage, strength, support, p_value, a list of items involved in the rule or a user-given filter function.

from bigml.association import Association
local_association = Association('association/502fdcff15526876610002435')
local_association.get_rules(item_list=["Edible"], min_p_value=0.3)

In this example, the only rules that will be returned by the get_rules method will be the ones that mention Edible and their p_value is greater or equal to 0.3.

The rules can also be stored in a CSV file using rules_CSV:

from bigml.association import Association
local_association = Association('association/502fdcff15526876610002435')
local_association.rules_CSV(file_name='/tmp/my_rules.csv',
                            min_strength=0.1)

This example will store the rules whose strength is bigger or equal to 0.1 in the /tmp/my_rules.csv file.

You can also obtain the list of items parsed in the dataset using the get_items method. You can also filter the results by field name, by item names and by a user-given function:

from bigml.association import Association
local_association = Association('association/502fdcff15526876610002435')
local_association.get_items(field="Cap Color",
                            names=["Brown cap", "White cap", "Yellow cap"])

This will recover the Item objects found in the Cap Color field for the names in the list, with their properties as described in the developers section

Local Association Sets

Using the local association object, you can predict the association sets related to an input data set:

local_association.association_set( \
    {"gender": "Female", "genres": "Adventure$Action", \
     "timestamp": 993906291, "occupation": "K-12 student",
     "zipcode": 59583, "rating": 3})
[{'item': {'complement': False,
           'count': 70,
           'field_id': u'000002',
           'name': u'Under 18'},
  'rules': ['000000'],
  'score': 0.0969181441561211},
 {'item': {'complement': False,
           'count': 216,
           'field_id': u'000007',
           'name': u'Drama'},
  'score': 0.025050115102862636},
 {'item': {'complement': False,
           'count': 108,
           'field_id': u'000007',
           'name': u'Sci-Fi'},
  'rules': ['000003'],
  'score': 0.02384578264599424},
 {'item': {'complement': False,
           'count': 40,
           'field_id': u'000002',
           'name': u'56+'},
  'rules': ['000008',
            '000020'],
  'score': 0.021845366022721312},
 {'item': {'complement': False,
           'count': 66,
           'field_id': u'000002',
           'name': u'45-49'},
  'rules': ['00000e'],
  'score': 0.019657155185835006}]

As in the local model predictions, producing local association sets can be done independently of BigML servers, so no cost or connection latencies are involved.

Local Topic Model

You can also instantiate a local version of a remote topic model.

from bigml.topicmodel import TopicModel
local_topic_model = TopicModel(
    'topicmodel/502fdbcf15526876210042435')

This will retrieve the remote topic model information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a TopicModel object that will be stored in the ./storage directory and you can use to obtain local topic distributions. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.topicmodel import TopicModel
from bigml.api import BigML

local_topic_model = TopicModel(
    'topicmodel/502fdbcf15526876210042435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote topic model JSON structure as previously retrieved to build the local topic model object:

from bigml.topicmodel import TopicModel
from bigml.api import BigML
api = BigML()
topic_model = api.get_topic_model(
    'topicmodel/502fdbcf15526876210042435',
    query_string='limit=-1')

local_topic_model = TopicModel(topic_model)

Note that in this example we used a limit=-1 query string for the topic model retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Topic Distributions

Using the local topic model object, you can predict the local topic distribution for an input data set:

local_topic_model.distribution({"Message": "Our mobile phone is free"})
[   {   'name': u'Topic 00', 'probability': 0.002627154266498529},
    {   'name': u'Topic 01', 'probability': 0.003257671290458176},
    {   'name': u'Topic 02', 'probability': 0.002627154266498529},
    {   'name': u'Topic 03', 'probability': 0.1968263976460698},
    {   'name': u'Topic 04', 'probability': 0.002627154266498529},
    {   'name': u'Topic 05', 'probability': 0.002627154266498529},
    {   'name': u'Topic 06', 'probability': 0.13692728036990331},
    {   'name': u'Topic 07', 'probability': 0.6419714165615805},
    {   'name': u'Topic 08', 'probability': 0.002627154266498529},
    {   'name': u'Topic 09', 'probability': 0.002627154266498529},
    {   'name': u'Topic 10', 'probability': 0.002627154266498529},
    {   'name': u'Topic 11', 'probability': 0.002627154266498529}]

As you can see, the topic distribution contains the name of the possible topics in the model and the associated probabilities.

Local Time Series

You can also instantiate a local version of a remote time series.

from bigml.timeseries import TimeSeries
local_time_series = TimeSeries(
    'timeseries/502fdbcf15526876210042435')

This will create a series of models from the remote time series information, using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials) and return a TimeSeries object that will be stored in the ./storage directory and you can use to obtain local forecasts. If you want to use a specific connection object for the remote retrieval or a different storage directory, you can set it as second parameter:

from bigml.timeseries import TimeSeries
from bigml.api import BigML

local_time_series = TimeSeries( \
    'timeseries/502fdbcf15526876210042435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote time series JSON structure as previously retrieved to build the local time series object:

from bigml.timeseries import TimeSeries
from bigml.api import BigML
api = BigML()
time_series = api.get_time_series( \
    'timeseries/502fdbcf15526876210042435',
    query_string='limit=-1')

local_time_series = TimeSeries(time_series)

Note that in this example we used a limit=-1 query string for the time series retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Forecasts

Using the local time series object, you can forecast any of the objective field values:

local_time_series.forecast({"Final": {"horizon": 5}, "Assignment": { \
    "horizon": 10, "ets_models": {"criterion": "aic", "limit": 2}}})
{u'000005': [
    {'point_forecast': [68.53181, 68.53181, 68.53181, 68.53181, 68.53181],
     'model': u'A,N,N'}],
 u'000001': [{'point_forecast': [54.776650000000004, 90.00943000000001,
                                 83.59285000000001, 85.72403000000001,
                                 72.87196, 93.85872, 84.80786, 84.65522,
                                 92.52545, 88.78403],
              'model': u'A,N,A'},
             {'point_forecast': [55.882820120000005, 90.5255466567616,
                                 83.44908577909621, 87.64524353046498,
                                 74.32914583152592, 95.12372848262932,
                                 86.69298716626228, 85.31630744944385,
                                 93.62385478607113, 89.06905451921818],
              'model': u'A,Ad,A'}]}

As you can see, the forecast contains the ID of the forecasted field, the computed points and the name of the models meeting the criterion. For more details about the available parameters, please check the API documentation.

Local PCAs

The PCA class will create a local version of a remote PCA.

from bigml.pca import PCA
local_pca = PCA(
    'pca/502fdbcf15526876210042435')

This will create an object that stores the remote information that defines the PCA, needed to generate projections to the new dimensionally reduced components. The remote resource is automatically downloaded the first time the the PCA is instantiated by using an implicitly built BigML() connection object (see the Authentication section for more details on how to set your credentials). The JSON that contains this information is stored in a ./storage directory, which is the default choice. If you want to use a specific connection object to define the credentials for the authentication in BigML or the directory where the JSON information is stored, you can set it as the second parameter:

from bigml.pca import PCA
from bigml.api import BigML

local_pca = PCA( \
    'timeseries/502fdbcf15526876210042435',
    api=BigML(my_username, my_api_key, storage="my_storage"))

You can also reuse a remote PCA JSON structure as previously retrieved to build the local PCA object:

from bigml.pca import PCA
from bigml.api import BigML
api = BigML()
time_series = api.get_pca( \
    'pca/502fdbcf15526876210042435',
    query_string='limit=-1')

local_pca = PCA(pca)

Note that in this example we used a limit=-1 query string for the PCA retrieval. This ensures that all fields are retrieved by the get method in the same call (unlike in the standard calls where the number of fields returned is limited).

Local Projections

Using the local PCA object, you can compute the projection of an input dataset into the new components:

local_pca.projection({"species": "Iris-versicolor"})
[6.03852, 8.35456, 5.04432, 0.75338, 0.06787, 0.03018]

You can use the max_components and variance_threshold arguments to limit the number of components generated. You can also use the full argument to produce a dictionary whose keys are the names of the generated components.

local_pca.projection({"species": "Iris-versicolor"}, full=yes)
{'PCA1': 6.03852, 'PCA2': 8.35456, 'PCA3': 5.04432, 'PCA4': 0.75338,
 'PCA5': 0.06787, 'PCA6': 0.03018}

As in the local model predictions, producing local projections can be done independently of BigML servers, so no cost or connection latencies are involved.

Local Forecasts

Using the local time series object, you can forecast any of the objective field values:

local_time_series.forecast({"Final": {"horizon": 5}, "Assignment": { \
    "horizon": 10, "ets_models": {"criterion": "aic", "limit": 2}}})
{u'000005': [
    {'point_forecast': [68.53181, 68.53181, 68.53181, 68.53181, 68.53181],
     'model': u'A,N,N'}],
 u'000001': [{'point_forecast': [54.776650000000004, 90.00943000000001,
                                 83.59285000000001, 85.72403000000001,
                                 72.87196, 93.85872, 84.80786, 84.65522,
                                 92.52545, 88.78403],
              'model': u'A,N,A'},
             {'point_forecast': [55.882820120000005, 90.5255466567616,
                                 83.44908577909621, 87.64524353046498,
                                 74.32914583152592, 95.12372848262932,
                                 86.69298716626228, 85.31630744944385,
                                 93.62385478607113, 89.06905451921818],
              'model': u'A,Ad,A'}]}

As you can see, the forecast contains the ID of the forecasted field, the computed points and the name of the models meeting the criterion. For more details about the available parameters, please check the API documentation.

Multi Models

Multi Models use a numbers of BigML remote models to build a local version that can be used to generate predictions locally. Predictions are generated combining the outputs of each model.

from bigml.api import BigML
from bigml.multimodel import MultiModel

api = BigML()

model = MultiModel([api.get_model(model['resource']) for model in
                   api.list_models(query_string="tags__in=my_tag")
                   ['objects']])

model.predict({"petal length": 3, "petal width": 1})

This will create a multi model using all the models that have been previously tagged with my_tag and predict by combining each model’s prediction. The combination method used by default is plurality for categorical predictions and mean value for numerical ones. You can also use confidence weighted:

model.predict({"petal length": 3, "petal width": 1}, method=1)

that will weight each vote using the confidence/error given by the model to each prediction, or even probability weighted:

model.predict({"petal length": 3, "petal width": 1}, method=2)

that weights each vote by using the probability associated to the training distribution at the prediction node.

There’s also a threshold method that uses an additional set of options: threshold and category. The category is predicted if and only if the number of predictions for that category is at least the threshold value. Otherwise, the prediction is plurality for the rest of predicted values.

An example of threshold combination method would be:

model.predict({'petal length': 0.9, 'petal width': 3.0}, method=3,
              options={'threshold': 3, 'category': 'Iris-virginica'})

When making predictions on a test set with a large number of models, batch_predict can be useful to log each model’s predictions in a separated file. It expects a list of input data values and the directory path to save the prediction files in.

model.batch_predict([{"petal length": 3, "petal width": 1},
                     {"petal length": 1, "petal width": 5.1}],
                    "data/predictions")

The predictions generated for each model will be stored in an output file in data/predictions using the syntax model_[id of the model]__predictions.csv. For instance, when using model/50c0de043b563519830001c2 to predict, the output file name will be model_50c0de043b563519830001c2__predictions.csv. An additional feature is that using reuse=True as argument will force the function to skip the creation of the file if it already exists. This can be helpful when using repeatedly a bunch of models on the same test set.

model.batch_predict([{"petal length": 3, "petal width": 1},
                     {"petal length": 1, "petal width": 5.1}],
                    "data/predictions", reuse=True)

Prediction files can be subsequently retrieved and converted into a votes list using batch_votes:

model.batch_votes("data/predictions")

which will return a list of MultiVote objects. Each MultiVote contains a list of predictions (e.g. [{'prediction': u'Iris-versicolor', 'confidence': 0.34, 'order': 0}, {'prediction': u'Iris-setosa', 'confidence': 0.25, 'order': 1}]). These votes can be further combined to issue a final prediction for each input data element using the method combine

for multivote in model.batch_votes("data/predictions"):
    prediction = multivote.combine()

Again, the default method of combination is plurality for categorical predictions and mean value for numerical ones. You can also use confidence weighted:

prediction = multivote.combine(1)

or probability weighted:

prediction = multivote.combine(2)

You can also get a confidence measure for the combined prediction:

prediction = multivolte.combine(0, with_confidence=True)

For classification, the confidence associated to the combined prediction is derived by first selecting the model’s predictions that voted for the resulting prediction and computing the weighted average of their individual confidence. Nevertheless, when probability weighted is used, the confidence is obtained by using each model’s distribution at the prediction node to build a probability distribution and combining them. The confidence is then computed as the wilson score interval of the combined distribution (using as total number of instances the sum of all the model’s distributions original instances at the prediction node)

In regression, all the models predictions’ confidences contribute to the weighted average confidence.

Local Ensembles

Remote ensembles can also be used locally through the Ensemble class. The simplest way to access an existing ensemble and using it to predict locally is:

from bigml.ensemble import Ensemble
ensemble = Ensemble('ensemble/5143a51a37203f2cf7020351')
ensemble.predict({"petal length": 3, "petal width": 1})

This is the simpler method to create a local Ensemble. The Ensemble('ensemble/5143a51a37203f2cf7020351') constructor, that fetches all the related JSON files and stores them in an ./storage directory. Next calls to Ensemble('ensemble/50c0de043b5635198300033c') will retrieve the files from this local storage, so that internet connection will only be needed the first time an Ensemble is built.

However, that method can only be used to work with the ensembles in our account in BigML. If we intend to use ensembles created under an Organization, then we need to provide the information about the project that the ensemble is included in. You need to provide a connection object for that:

from bigml.ensemble import Ensemble
from bigml.api import BigML

# connection object that informs about the project ID and the
# directory where the ensemble will be stored for local use

api = BigML(project="project/5143a51a37203f2cf7020001",
            storage="my_storage_directory")

ensemble = Ensemble('ensemble/5143a51a37203f2cf7020351', api=api)
ensemble.predict({"petal length": 3, "petal width": 1})

The local ensemble object can be used to manage the three types of ensembles: Decision Forests (bagging or random) and the ones using Boosted Trees. Also, you can choose the storage directory or even avoid storing at all. The àpi connection object controls the storage strategy through the storage argument.

from bigml.api import BigML
from bigml.ensemble import Ensemble

# api connection using a user-selected storage
api = BigML(storage='./my_storage')

# creating ensemble
ensemble = api.create_ensemble('dataset/5143a51a37203f2cf7000972')

# Ensemble object to predict
ensemble = Ensemble(ensemble, api)
ensemble.predict({"petal length": 3, "petal width": 1},
                 operating_kind="votes")

In this example, we create a new ensemble and store its information in the ./my_storage folder. Then this information is used to predict locally using the number of votes (one per model) backing each category.

The operating_kind argument overrides the legacy method argument, which was previously used to define the combiner for the models predictions.

Similarly, local ensembles can also be created by giving a list of models to be combined to issue the final prediction (note: only random decision forests and bagging ensembles can be built using this method):

from bigml.ensemble import Ensemble
ensemble = Ensemble(['model/50c0de043b563519830001c2', \
                     'model/50c0de043b5635198300031b')]
ensemble.predict({"petal length": 3, "petal width": 1})

or even a JSON file that contains the ensemble resource:

from bigml.api import BigML
api = BigML()
api.export("ensemble/50c0de043b5635198300033c",
           "my_directory/my_ensemble.json")

from bigml.ensemble import Ensemble
local_ensemble = Ensemble("./my_directory/my_ensemble.json")

Note: the ensemble JSON structure is not self-contained, meaning that it contains references to the models that the ensemble is build of, but not the information of the models themselves. To use an ensemble locally with no connection to the internet, you must make sure that not only a local copy of the ensemble JSON file is available in your computer, but also the JSON files corresponding to the models in it. The export method takes care of storing the information of every model in the ensemble and storing it in the same directory as the ensemble JSON file. The Ensemble class will also look up for the model files in the same directory when using a path to an ensemble file as argument.

If you have no memory limitations you can create the ensemble from a list of local model objects. Then, local model objects will be always in memory and will only be instantiated once. This will increase performance for large ensembles:

from bigml.model import Model
model_ids = ['model/50c0de043b563519830001c2', \
             'model/50c0de043b5635198300031b']
local_models = [Model(model_id) for model_id in model_ids]
local_ensemble = Ensemble(local_models)

Local Ensemble caching

Ensembles can become quite large objects and demand large memory resources. If your usual scenario is using many of them constantly in a disordered way, the best strategy is setting up a cache system to store them. The local ensemble class provides helpers to interact with that cache. Here’s an example using Redis.

from ensemble import Ensemble
import redis
r = redis.Redis()
# First build as you would any core Ensemble object:
local_ensemble = Ensemble('ensemble/5126965515526876630001b2')
# Store a serialized version in Redis
ensemble.dump(cache_set=r.set)
# (retrieve the external rep from its convenient place)
# Speedy Build from external rep
local_ensemble = Ensemble('ensemble/5126965515526876630001b2', \
    cache_get=r.get)
# Get scores same as always:
local_ensemble.predict({"src_bytes": 350})

Local Ensemble’s Predictions

As in the local model’s case, you can use the local ensemble to create new predictions for your test data, and set some arguments to configure the final output of the predict method.

The predictions’ structure will vary depending on the kind of ensemble used. For Decision Forests local predictions will just contain the ensemble’s final prediction if no other argument is used.

from bigml.ensemble import Ensemble
ensemble = Ensemble('ensemble/5143a51a37203f2cf7020351')
ensemble.predict({"petal length": 3, "petal width": 1})
u'Iris-versicolor'

The final prediction of an ensemble is determined by aggregating or selecting the predictions of the individual models therein. For classifications, the most probable class is returned if no especial operating method is set. Using full=True you can see both the predicted output and the associated probability:

from bigml.ensemble import Ensemble
ensemble = Ensemble('ensemble/5143a51a37203f2cf7020351')
ensemble.predict({"petal length": 3, "petal width": 1}, \
                 full=True)

{'prediction': u'Iris-versicolor',
 'probability': 0.98566}

In general, the prediction in a classification will be one amongst the list of categories in the objective field. When each model in the ensemble is used to predict, each category has a confidence, a probability or a vote associated to this prediction. Then, through the collection of models in the ensemble, each category gets an averaged confidence, probabiity and number of votes. Thus you can decide whether to operate the ensemble using the confidence, the probability or the votes so that the predicted category is the one that scores higher in any of these quantities. The criteria can be set using the operating_kind option (default is set to probability):

ensemble.predict({"petal length": 3, "petal width": 1}, \
                 operating_kind="votes")

Regression will generate a predictiona and an associated error, however Boosted Trees don’t have an associated confidence measure, so only the prediction will be obtained in this case.

For consistency of interface with the Model class, as well as between boosted and non-boosted ensembles, local Ensembles again have a predict_probability method. This takes the same optional arguments as Model.predict: missing_strategy and compact. As with local Models, if compact is False (the default), the output is a list of maps, each with the keys prediction and probability mapped to the class name and its associated probability.

So, for example:

ensemble.predict_probability({"petal length": 3, "petal width": 1})

[{'category': u'Iris-setosa', 'probability': 0.006733220044732548},
 {'category': u'Iris-versicolor', 'probability': 0.9824478534614787},
 {'category': u'Iris-virginica', 'probability': 0.0108189264937886}]

If compact is True, only the probabilities themselves are returned, as a list in class name order, again, as is the case with local Models.

Operating point predictions are also available for local ensembles and an example of it would be:

operating_point = {"kind": "probability",
                   "positive_class": "True",
                   "threshold": 0.8};
prediction = local_ensemble.predict(inputData,
                                    operating_point=operating_point)

You can check the Operating point’s predictions section to learn about operating points. For ensembles, three kinds of operating points are available: votes, probability and confidence. Votes will use as threshold the number of models in the ensemble that vote for the positive class. The other two are already explained in the above mentioned section.

Local Ensemble Predictor

Predictions can take longer when the ensemble is formed by a large number of models or when its models have a high number of nodes. In these cases, predictions’ speed can be increased and memory usage minimized by using the EnsemblePredictor object. The basic example to build it is:

from bigml.ensemblepredictor import EnsemblePredictor
ensemble = EnsemblePredictor('ensemble/5143a51a37203f2cf7020351',
                             "./model_fns_directory")
ensemble.predict({"petal length": 3, "petal width": 1}, full=True)
{'prediction': u'Iris-versicolor', 'confidence': 0.91519}

This constructor has two compulsory attributes: then ensemble ID (or the corresponding API response) and the path to a directory that contains a file per each of the ensemble models. Each file stores the predict function needed to obtain the model’s predictions. As in the Ensemble object, you can also add an api argument with the connection to be used to download the ensemble’s JSON information.

The functions stored in this directory are generated automatically the first time you instantiate the ensemble. Once they are generated, the functions are retrieved from the directory.

Note that only last prediction missings strategy is available for these predictions and the combiners available are plurality, confidence and distribution but no operating_kind or operating_point options are provided at present.

Local Supervised Model

There’s a general class that will allow you to predict using any supervised model resource, regardless of its particular type (model, ensemble, logistic regression or deepnet).

The SupervisedModel object will retrieve the resource information and instantiate the corresponding local object, so that you can use its predict method to produce local predictions:

from bigml.supervised import SupervisedModel
local_supervised_1 = SupervisedModel( \
    "logisticregression/5143a51a37203f2cf7020351")
local_supervised_2 = SupervisedModel( \
    "model/5143a51a37203f2cf7020351")
input_data = {"petal length": 3, "petal width": 1}
logistic_regression_prediction = local_supervised_1.predict(input_data)
model_prediction = local_supervised_2.predict(input_data)

Local caching

All local models can use an external cache system to manage memory storage and recovery. The get and set functions of the cache manager should be passed to the constructor or dump function. Here’s an example on how to cache a linear regression:

from bigml.linear import LinearRegression lm = LinearRegression(“linearregression/5e827ff85299630d22007198”) lm.predict({“petal length”: 4, “sepal length”:4, “petal width”: 4,

“sepal width”: 4, “species”: “Iris-setosa”}, full=True)

import redis r = redis.Redis() # First build as you would any core LinearRegression object: # Store a serialized version in Redis lm.dump(cache_set=r.set) # (retrieve the external rep from its convenient place) # Speedy Build from external rep lm = LinearRegression(“linearregression/5e827ff85299630d22007198”,

cache_get=r.get)

# Get predictions same as always: lm.predict({“petal length”: 4, “sepal length”:4, “petal width”: 4,

“sepal width”: 4, “species”: “Iris-setosa”}, full=True)

Rule Generation

You can also use a local model to generate a IF-THEN rule set that can be very helpful to understand how the model works internally.

local_model.rules()
IF petal_length > 2.45 AND
    IF petal_width > 1.65 AND
        IF petal_length > 5.05 THEN
            species = Iris-virginica
        IF petal_length <= 5.05 AND
            IF sepal_width > 2.9 AND
                IF sepal_length > 5.95 AND
                    IF petal_length > 4.95 THEN
                        species = Iris-versicolor
                    IF petal_length <= 4.95 THEN
                        species = Iris-virginica
                IF sepal_length <= 5.95 THEN
                    species = Iris-versicolor
            IF sepal_width <= 2.9 THEN
                species = Iris-virginica
    IF petal_width <= 1.65 AND
        IF petal_length > 4.95 AND
            IF sepal_length > 6.05 THEN
                species = Iris-virginica
            IF sepal_length <= 6.05 AND
                IF sepal_width > 2.45 THEN
                    species = Iris-versicolor
                IF sepal_width <= 2.45 THEN
                    species = Iris-virginica
        IF petal_length <= 4.95 THEN
            species = Iris-versicolor
IF petal_length <= 2.45 THEN
    species = Iris-setosa

Python, Tableau and Hadoop-ready Generation

If you prefer, you can also generate a Python function that implements the model and that can be useful to make the model actionable right away with local_model.python().

local_model.python()
def predict_species(sepal_length=None,
                    sepal_width=None,
                    petal_length=None,
                    petal_width=None):
    """ Predictor for species from model/50a8e2d9eabcb404d2000293

        Predictive model by BigML - Machine Learning Made Easy
    """
    if (petal_length is None):
        return 'Iris-virginica'
    if (petal_length <= 2.45):
        return 'Iris-setosa'
    if (petal_length > 2.45):
        if (petal_width is None):
            return 'Iris-virginica'
        if (petal_width <= 1.65):
            if (petal_length <= 4.95):
                return 'Iris-versicolor'
            if (petal_length > 4.95):
                if (sepal_length is None):
                    return 'Iris-virginica'
                if (sepal_length <= 6.05):
                    if (petal_width <= 1.55):
                        return 'Iris-virginica'
                    if (petal_width > 1.55):
                        return 'Iris-versicolor'
                if (sepal_length > 6.05):
                    return 'Iris-virginica'
        if (petal_width > 1.65):
            if (petal_length <= 5.05):
                if (sepal_width is None):
                    return 'Iris-virginica'
                if (sepal_width <= 2.9):
                    return 'Iris-virginica'
                if (sepal_width > 2.9):
                    if (sepal_length is None):
                        return 'Iris-virginica'
                    if (sepal_length <= 6.4):
                        if (sepal_length <= 5.95):
                            return 'Iris-versicolor'
                        if (sepal_length > 5.95):
                            return 'Iris-virginica'
                    if (sepal_length > 6.4):
                        return 'Iris-versicolor'
            if (petal_length > 5.05):
                return 'Iris-virginica'

The local.python(hadoop=True) call will generate the code that you need for the Hadoop map-reduce engine to produce batch predictions using Hadoop streaming . Saving the mapper and reducer generated functions in their corresponding files (let’s say /home/hduser/hadoop_mapper.py and /home/hduser/hadoop_reducer.py) you can start a Hadoop job to generate predictions by issuing the following Hadoop command in your system console:

bin/hadoop jar contrib/streaming/hadoop-*streaming*.jar \
-file /home/hduser/hadoop_mapper.py -mapper hadoop_mapper.py \
-file /home/hduser/hadoop_reducer.py -reducer hadoop_reducer.py \
-input /home/hduser/hadoop/input.csv \
-output /home/hduser/hadoop/output_dir

assuming you are in the Hadoop home directory, your input file is in the corresponding dfs directory (/home/hduser/hadoop/input.csv in this example) and the output will be placed at /home/hduser/hadoop/output_dir (inside the dfs directory).

Tableau-ready rules are also available through local_model.tableau() for all the models except those that use text predictors.

local_model.tableau()
IF ISNULL([petal width]) THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]>1.75 AND ISNULL([petal length]) THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]>1.75 AND [petal length]>4.85 THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]>1.75 AND [petal length]<=4.85 AND ISNULL([sepal width]) THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]>1.75 AND [petal length]<=4.85 AND [sepal width]>3.1 THEN 'Iris-versicolor'
ELSEIF [petal width]>0.8 AND [petal width]>1.75 AND [petal length]<=4.85 AND [sepal width]<=3.1 THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]<=1.75 AND ISNULL([petal length]) THEN 'Iris-versicolor'
ELSEIF [petal width]>0.8 AND [petal width]<=1.75 AND [petal length]>4.95 AND [petal width]>1.55 AND [petal length]>5.45 THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]<=1.75 AND [petal length]>4.95 AND [petal width]>1.55 AND [petal length]<=5.45 THEN 'Iris-versicolor'
ELSEIF [petal width]>0.8 AND [petal width]<=1.75 AND [petal length]>4.95 AND [petal width]<=1.55 THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]<=1.75 AND [petal length]<=4.95 AND [petal width]>1.65 THEN 'Iris-virginica'
ELSEIF [petal width]>0.8 AND [petal width]<=1.75 AND [petal length]<=4.95 AND [petal width]<=1.65 THEN 'Iris-versicolor'
ELSEIF [petal width]<=0.8 THEN 'Iris-setosa'
END

Summary generation

You can also print the model from the point of view of the classes it predicts with local_model.summarize(). It shows a header section with the training data initial distribution per class (instances and percentage) and the final predicted distribution per class.

Then each class distribution is detailed. First a header section shows the percentage of the total data that belongs to the class (in the training set and in the predicted results) and the rules applicable to all the the instances of that class (if any). Just after that, a detail section shows each of the leaves in which the class members are distributed. They are sorted in descending order by the percentage of predictions of the class that fall into that leaf and also show the full rule chain that leads to it.

Data distribution:
    Iris-setosa: 33.33% (50 instances)
    Iris-versicolor: 33.33% (50 instances)
    Iris-virginica: 33.33% (50 instances)


Predicted distribution:
    Iris-setosa: 33.33% (50 instances)
    Iris-versicolor: 33.33% (50 instances)
    Iris-virginica: 33.33% (50 instances)


Field importance:
    1. petal length: 53.16%
    2. petal width: 46.33%
    3. sepal length: 0.51%
    4. sepal width: 0.00%


Iris-setosa : (data 33.33% / prediction 33.33%) petal length <= 2.45
    · 100.00%: petal length <= 2.45 [Confidence: 92.86%]


Iris-versicolor : (data 33.33% / prediction 33.33%) petal length > 2.45
    · 94.00%: petal length > 2.45 and petal width <= 1.65 and petal length <= 4.95 [Confidence: 92.44%]
    · 2.00%: petal length > 2.45 and petal width <= 1.65 and petal length > 4.95 and sepal length <= 6.05 and petal width > 1.55 [Confidence: 20.65%]
    · 2.00%: petal length > 2.45 and petal width > 1.65 and petal length <= 5.05 and sepal width > 2.9 and sepal length > 6.4 [Confidence: 20.65%]
    · 2.00%: petal length > 2.45 and petal width > 1.65 and petal length <= 5.05 and sepal width > 2.9 and sepal length <= 6.4 and sepal length <= 5.95 [Confidence: 20.65%]


Iris-virginica : (data 33.33% / prediction 33.33%) petal length > 2.45
    · 76.00%: petal length > 2.45 and petal width > 1.65 and petal length > 5.05 [Confidence: 90.82%]
    · 12.00%: petal length > 2.45 and petal width > 1.65 and petal length <= 5.05 and sepal width <= 2.9 [Confidence: 60.97%]
    · 6.00%: petal length > 2.45 and petal width <= 1.65 and petal length > 4.95 and sepal length > 6.05 [Confidence: 43.85%]
    · 4.00%: petal length > 2.45 and petal width > 1.65 and petal length <= 5.05 and sepal width > 2.9 and sepal length <= 6.4 and sepal length > 5.95 [Confidence: 34.24%]
    · 2.00%: petal length > 2.45 and petal width <= 1.65 and petal length > 4.95 and sepal length <= 6.05 and petal width <= 1.55 [Confidence: 20.65%]

You can also use local_model.get_data_distribution() and local_model.get_prediction_distribution() to obtain the training and prediction basic distribution information as a list (suitable to draw histograms or any further processing). The tree nodes’ information (prediction, confidence, impurity and distribution) can also be retrieved in a CSV format using the method local_model.tree_CSV(). The output can be sent to a file by providing a file_name argument or used as a list.

Local ensembles have a local_ensemble.summarize() method too, the output in this case shows only the data distribution (only available in Decision Forests) and field importance sections.

For local clusters, the local_cluster.summarize() method prints also the data distribution, the training data statistics per cluster and the basic intercentroid distance statistics. There’s also a local_cluster.statistics_CSV(file_name) method that store in a CSV format the values shown by the summarize() method. If no file name is provided, the function returns the rows that would have been stored in the file as a list.