def data_fetcher(self):
return DataFetcher(
DataSpec(
time_series={
"ts1":
TimeSeriesSpec(id=1234,
start=3000,
end=5000,
aggregate="average",
granularity="1s"),
"ts2":
TimeSeriesSpec(id=2345,
start=3000,
end=5000,
aggregate="max",
granularity="1s"),
"ts3":
TimeSeriesSpec(id=3456,
start=4000,
end=9000,
aggregate="min",
granularity="1s"),
"ts4":
TimeSeriesSpec(external_id="abc",
start=3000,
end=5000,
aggregate="average",
granularity="1m"),
"ts5":
TimeSeriesSpec(id=5678, start=6000, end=8000),
}))
def train(open_artifact, data_spec):
"""
open_artifact:
The train method must accept a open_artifact argument. This is a function
that works the same way as the builtin open(), except it reads from
and writes to the root of a special storage location in the cloud
that belongs to the current model version.
data_spec:
An argument we pass in ourself when we initiate the training.
api_key, project:
Optional arguments that are passed in automatically from Model
Hosting if you specify them.
"""
data_fetcher = DataFetcher(data_spec)
data_fetcher.files.fetch("data")
data_fetcher.files.fetch("target")
X = pd.read_csv("data")
y = pd.read_csv("target")
# Add a feature of constant value 1
X.insert(0, "f0", 1)
# Least squares
coefficients = pd.DataFrame(np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y),
columns=["beta_hat"])
# Persist our result
with open_artifact("coefficients.csv", "w") as f:
coefficients.to_csv(f, index=False)
def data_fetcher(self, file_ids) -> DataFetcher:
return DataFetcher(
DataSpec(
files={
"a": FileSpec(id=file_ids["a.txt"]),
"a_duplicate": FileSpec(id=file_ids["a.txt"]),
"b": FileSpec(id=file_ids["b.txt"]),
"big": FileSpec(id=file_ids["big.txt"]),
}))
def data_fetcher(self, ts_ids, now) -> DataFetcher:
one_hour_ago = now - 3600 * 1000
return DataFetcher(
DataSpec(
time_series={
"constant3":
TimeSeriesSpec(
id=ts_ids["constant_3"], start=one_hour_ago, end=now),
"constant3_duplicate":
TimeSeriesSpec(
id=ts_ids["constant_3"], start=one_hour_ago, end=now),
"constant3_avg_1s":
TimeSeriesSpec(id=ts_ids["constant_3"],
start=one_hour_ago,
end=now,
aggregate="average",
granularity="1s"),
"constant3_avg_1s_duplicate":
TimeSeriesSpec(id=ts_ids["constant_3"],
start=one_hour_ago,
end=now,
aggregate="average",
granularity="1s"),
"constant3_avg_1m":
TimeSeriesSpec(id=ts_ids["constant_3"],
start=one_hour_ago,
end=now,
aggregate="average",
granularity="1m"),
"constant3_max_1m":
TimeSeriesSpec(id=ts_ids["constant_3"],
start=one_hour_ago,
end=now,
aggregate="max",
granularity="1m"),
"constant4":
TimeSeriesSpec(
id=ts_ids["constant_4"], start=one_hour_ago, end=now),
"constant4_avg_1s":
TimeSeriesSpec(id=ts_ids["constant_4"],
start=one_hour_ago,
end=now,
aggregate="average",
granularity="1s"),
"constant5_min_1s":
TimeSeriesSpec(id=ts_ids["constant_5"],
start=one_hour_ago,
end=now,
aggregate="min",
granularity="1s"),
"constant6_max_1s":
TimeSeriesSpec(id=ts_ids["constant_6"],
start=one_hour_ago,
end=now,
aggregate="max",
granularity="1s"),
}))
def test_fetch_datapoints_single_many_datapoints(self, ts_ids, now):
data_fetcher = DataFetcher(
DataSpec(
time_series={
"constant3":
TimeSeriesSpec(id=ts_ids["constant_3"],
start=now - 48 * 3600 * 1000,
end=now)
}))
df = data_fetcher.time_series.fetch_datapoints("constant3")
self.assert_data_frame(df, ["value"], {"value": 3})
def test_fetch_datapoints_many_time_series(self, ts_ids, now):
data_fetcher = DataFetcher(
DataSpec(
time_series={
"constant{}".format(i): TimeSeriesSpec(
id=ts_ids["constant_{}".format(i)],
start=now - 3600 * 1000,
end=now)
for i in range(100)
}))
dfs = data_fetcher.time_series.fetch_datapoints(
["constant{}".format(i) for i in range(100)])
for i in range(100):
self.assert_data_frame(dfs["constant{}".format(i)], ["value"],
{"value": i})
def test_fetch_datapoints_multiple_many_datapoints(self, ts_ids, now):
data_fetcher = DataFetcher(
DataSpec(
time_series={
"constant3":
TimeSeriesSpec(id=ts_ids["constant_3"],
start=now - 48 * 3600 * 1000,
end=now),
"constant4":
TimeSeriesSpec(id=ts_ids["constant_4"],
start=now - 48 * 3600 * 1000,
end=now),
}))
dfs = data_fetcher.time_series.fetch_datapoints(
["constant3", "constant4"])
self.assert_data_frame(dfs["constant3"], ["value"], {"value": 3})
self.assert_data_frame(dfs["constant4"], ["value"], {"value": 4})
def predict(self, instance):
"""
instance:
Since we're doing scheduled prediction, this will be a data spec
describing the data we should do prediction on.
Note that it's also possible to take api_key and project in as
optional arguments here.
"""
dts = DataFetcher(instance)
df = dts.time_series.fetch_dataframe(["temp", "pressure", "rpm"]).dropna()
X = df[["temp", "pressure", "rpm"]].values
df["production_rate"] = self.regressor.predict(X)
# For scheduled prediction we need to return output on the format:
# {
# "timeSeries":
# { "production_rate": [(t0, p0), (t1, p1), (t2, p2), ...] }
# }
# We can use a model hosting utilities method to convert our dataframe
# to this format.
return to_output(df[["timestamp", "production_rate"]])
start=start,
end=end,
aggregate=aggregate,
granularity=granularity),
"gas_integ_time":
TimeSeriesSpec(4988486819178408,
start=start,
end=end,
aggregate=aggregate,
granularity=granularity),
"gas_gain":
TimeSeriesSpec(3658191334677419, start=start, end=end),
})
# Now lets fetch the data for our "gas_auto" and "gas_external" time series
data_fetcher = DataFetcher(data_spec,
api_key=os.getenv("COGNITE_OID_API_KEY"),
project="publicdata")
df = data_fetcher.time_series.fetch_dataframe(["gas_auto", "gas_external"])
print(df.head())
# When using fetch_dataframe all specified time series must have the same start, end, and granularity
# To fetch data from times series with different specs, we can use the following method
dfs = data_fetcher.time_series.fetch_datapoints(["gas_integ_time", "gas_gain"])
print(dfs["gas_gain"].head())
print(dfs["gas_integ_time"].head())
def predict(self, instance):
data_fetcher = DataFetcher(instance)
df = data_fetcher.time_series.fetch_dataframe(["x1", "x2"])
df["y"] = df["x2"] / df["x1"]
return to_output(df.size)
def predict(self, instance):
data_fetcher = DataFetcher(instance)
df = data_fetcher.time_series.fetch_dataframe(["x1", "x2"])
df["y"] = (df["x1"] + df["x2"]) / math.pi
return to_output(df[["y", "timestamp"]])
def data_fetcher(self, file_specs, rsps):
data_spec = DataSpec(files=file_specs)
data_fetcher = DataFetcher(data_spec)
return data_fetcher
def test_get_data_spec():
data_spec = DataSpec(files={"f1": FileSpec(id=123)})
getted_data_spec = DataFetcher(data_spec).get_data_spec()
getted_data_spec.files["f1"].id = 234
assert data_spec.files["f1"].id == 123
def test_invalid_spec_type():
with pytest.raises(SpecValidationError, match="has to be of type"):
DataFetcher(123)
def test_empty_data_spec(rsps, data_spec):
data_fetcher = DataFetcher(data_spec)
assert data_fetcher.get_data_spec() == DataSpec()
end=end,
aggregate=aggregate,
granularity=granularity),
"gas_integ_time":
TimeSeriesSpec(id=4988486819178408,
start=start,
end=end,
aggregate=aggregate,
granularity=granularity),
"gas_gain":
TimeSeriesSpec(id=3658191334677419, start=start, end=end),
})
# Now lets fetch the data for our "gas_auto" and "gas_external" time series
data_fetcher = DataFetcher(data_spec,
api_key=os.getenv("COGNITE_OID_API_KEY"),
project="publicdata",
client_name="test-client")
df = data_fetcher.time_series.fetch_dataframe(["gas_auto", "gas_external"])
print(df.head())
# When using fetch_dataframe all specified time series must have the same start, end, and granularity
# To fetch data from times series with different specs, we can use the following method
dfs = data_fetcher.time_series.fetch_datapoints(["gas_integ_time", "gas_gain"])
print(dfs["gas_gain"].head())
print(dfs["gas_integ_time"].head())
def predict(self, instance):
data_fetcher = DataFetcher(instance,
client_name="simple-transform-client")
df = data_fetcher.time_series.fetch_dataframe(["x1", "x2"])
df["y"] = (df["x1"] + df["x2"]) / math.pi
return to_output(df[["y"]])
def predict(self, instance):
data_fetcher = DataFetcher(instance, client_name="cprfix-client")
df = data_fetcher.time_series.fetch_dataframe(["x1", "x2"])
df["y"] = df["x2"] / df["x1"]
return to_output(df[["y"]])
