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Methods defined here:
- __init__(self, vertices=None, model_key=None, sequence_key=None, observed_key=None, state_key=None, skip_key=None, batch_size=None, vertices_sequence_column=None, vertices_partition_column=None, vertices_order_column=None)
- DESCRIPTION:
The HMMSupervised function runs on the SQL-GR framework. The function
can produce multiple HMM models simultaneously, where each model is
learned from a set of sequences and where each sequence represents a
vertex.
PARAMETERS:
vertices:
Required Argument.
Specifies the teradataml DataFrame containing the vertex data.
vertices_partition_column:
Required Argument.
Specifies Partition By columns for vertices.
Values to this argument can be provided as list, if multiple columns
are used for partition.
Note:
1. This argument must contain the name of the column specified in
'sequence_key' argument.
2. This argument should contain the name of the column specified in
'model_key', if 'model_key' argument is used, and it must be
the first column followed by the name of the column specified
in 'sequence_key'.
Types: str OR list of Strings (str)
vertices_order_column:
Required Argument.
Specifies Order By columns for vertices.
Values to this argument can be provided as list, if multiple columns
are used for ordering.
Note: This argument must contain the name of the column, containing
time ordered sequence, as one of its columns.
Types: str OR list of Strings (str)
model_key:
Optional Argument.
Specifies the name of the column that contains the model attribute.
The values in the column can be integers or strings.
Note: The 'vertices_partition_column' argument should contain the name
of the column specified in this argument.
Types: str
sequence_key:
Required Argument.
Specifies the name of the column that contains the sequence attribute. The
sequence_key must be a sequence attribute in the
vertices_partition_column. A sequence (value in this column) must contain more
than two observation symbols. Each sequence represent a vertex.
Types: str
observed_key:
Required Argument.
Specifies the name of the column that contains the observed symbols. The
function scans the input teradataml DataFrame to find all possible
observed symbols.
Note: Observed symbols are case-sensitive.
Types: str
state_key:
Required Argument.
Specifies the column containing state attributes. You can specify multiple
states. The states are case-sensitive.
Types: str
skip_key:
Optional Argument.
Specifies the name of the column whose values determine whether the function
skips the row. The function skips the row if the value is "true",
"yes", "y", or "1". The function does not skip the row if the value
is "false", "f", "no", "n", "0", or NULL.
Types: str
batch_size:
Optional Argument.
Specifies the number of models to process. The size must be positive. If the
batch size is not specified, the function avoids out-of-memory errors
by determining the appropriate size. If the batch size is specified
and there is insufficient free memory, the function reduces the batch
size. The batch size is determined dynamically, based on the memory
conditions. For example, at time T1, the specified batch size 1000
might be adjusted to 980, and at time T2, the batch size might be
adjusted to 800.
Types: int
vertices_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "vertices". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of HMMSupervised.
Output teradataml DataFrames can be accessed using attribute
references, such as HMMSupervisedObj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. output_initialstate_table
2. output_statetransition_table
3. output_emission_table
4. output
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("hmmsupervised", "customer_loyalty")
#
# "customer_loyalty" dataset contains events that are related to customer transaction.
# Each event comprises of the time elapsed since the last transaction and
# the amount spent compared amount spent in the last transaction
#
# Time elapsed since the last transaction:
# small(S), medium(M) and large(L)
# and the amount spent compared amount spent in the last transaction:
# less(L), about same(S) and more(M).
#
# So there are 9 possible combinations, resulting in 9 events.
# For example, the event SM implies a transaction, where the time elapsed
# since the last transaction is small and the customer spent more than last time.
#
# Datset also contains 3 hidden states corresponding to 3 levels of loyalty:
# low(L), normal(N), high(H).
# Create teradataml DataFrame objects.
customer_loyalty = DataFrame.from_table("customer_loyalty")
# Example 1 - Train a HMM Supervised model on the customer loyalty dataset
HMMSupervised_out = HMMSupervised(vertices = customer_loyalty,
vertices_partition_column = ["user_id", "seq_id"],
vertices_order_column = ["user_id", "seq_id", "purchase_date"],
model_key = "user_id",
sequence_key = "seq_id",
observed_key = "observation",
state_key = "loyalty_level"
)
# Print the results.
print(HMMSupervised_out.output_initialstate_table)
print(HMMSupervised_out.output_statetransition_table)
print(HMMSupervised_out.output_emission_table)
print(HMMSupervised_out.output)
- __repr__(self)
- Returns the string representation for a HMMSupervised class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
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