traceml


Nametraceml JSON
Version 1.1.1 PyPI version JSON
download
home_pagehttps://github.com/polyaxon/traceml
SummaryEngine for ML/Data tracking, visualization, dashboards, and model UI for Polyaxon.
upload_time2024-02-29 09:34:15
maintainerPolyaxon, Inc.
docs_urlNone
authorPolyaxon, Inc.
requires_python>=3.8
licenseApache 2.0
keywords polyaxon aws s3 microsoft azure google cloud storage gcs deep-learning machine-learning data-science neural-networks artificial-intelligence ai reinforcement-learning kubernetes aws microsoft azure google cloud tensorflow pytorch matplotlib plotly visualization analytics
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            [![License: Apache 2](https://img.shields.io/badge/License-apache2-green.svg)](LICENSE)
[![TraceML](https://github.com/polyaxon/traceml/actions/workflows/traceml.yml/badge.svg)](https://github.com/polyaxon/traceml/actions/workflows/traceml.yml)
[![Slack](https://img.shields.io/badge/chat-on%20slack-aadada.svg?logo=slack&longCache=true)](https://polyaxon.com/slack/)
[![Docs](https://img.shields.io/badge/docs-stable-brightgreen.svg?style=flat)](https://polyaxon.com/docs/)
[![GitHub](https://img.shields.io/badge/issue_tracker-github-blue?logo=github)](https://github.com/polyaxon/polyaxon/issues)
[![GitHub](https://img.shields.io/badge/roadmap-github-blue?logo=github)](https://github.com/polyaxon/polyaxon/milestones)

<a href="https://polyaxon.com"><img src="https://raw.githubusercontent.com/polyaxon/polyaxon/master/artifacts/packages/traceml.svg" width="125" height="125" align="right" /></a>

# TraceML

Engine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.

## Install

```bash
pip install traceml
```

If you would like to use the tracking features, you need to install `polyaxon` as well:

```bash
pip install polyaxon traceml
```

## [WIP] Local sandbox

> Coming soon

## Offline usage

You can enable the offline mode to track runs without an  API:

```bash
export POLYAXON_OFFLINE="true"
```

Or passing the offline flag

```python
from traceml import tracking

tracking.init(..., is_offline=True, ...)
```

## Simple usage in a Python script

```python
import random

import traceml as tracking

tracking.init(
    is_offline=True,
    project='quick-start',
    name="my-new-run",
    description="trying TraceML",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

# Tracking some data refs
tracking.log_data_ref(content=X_train, name='x_train')
tracking.log_data_ref(content=y_train, name='y_train')

# Tracking inputs
tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)

def get_loss(step):
    result = 10 / (step + 1)
    noise = (random.random() - 0.5) * 0.5 * result
    return result + noise

# Track metrics
for step in range(100):
    loss = get_loss(step)
    tracking.log_metrics(
    loss=loss,
    accuracy=(100 - loss) / 100.0,
)

# Track some one time results
tracking.log_outputs(validation_score=0.66)

# Optionally manually stop the tracking process
tracking.stop()
```

## Integration with deep learning and machine learning libraries and frameworks

### Keras

You can use TraceML's callback to automatically save all metrics and collect outputs and models, you can also track additional information using the logging methods:

```python
from traceml import tracking
from traceml.integrations.keras import Callback

tracking.init(
    is_offline=True,
    project='tracking-project',
    name="keras-run",
    description="trying TraceML & Keras",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)
tracking.log_data_ref(content=x_train, name='x_train')
tracking.log_data_ref(content=y_train, name='y_train')
tracking.log_data_ref(content=x_test, name='x_test')
tracking.log_data_ref(content=y_test, name='y_test')

# ...

model.fit(
    x_train,
    y_train,
    validation_data=(X_test, y_test),
    epochs=epochs,
    batch_size=100,
    callbacks=[Callback()],
)
```

### PyTorch

You can log metrics, inputs, and outputs of Pytorch experiments using the tracking module:

```python
from traceml import tracking

tracking.init(
    is_offline=True,
    project='tracking-project',
    name="pytorch-run",
    description="trying TraceML & PyTorch",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)

# Metrics
for batch_idx, (data, target) in enumerate(train_loader):
    output = model(data)
    loss = F.nll_loss(output, target)
    loss.backward()
    optimizer.step()
    tracking.log_metrics(loss=loss)

asset_path = tracking.get_outputs_path('model.ckpt')
torch.save(model.state_dict(), asset_path)

# log model
tracking.log_artifact_ref(asset_path, framework="pytorch", ...)
```

### Tensorflow

You can log metrics, outputs, and models of Tensorflow experiments and distributed Tensorflow experiments using the tracking module:

```python
from traceml import tracking
from traceml.integrations.tensorflow import Callback

tracking.init(
    is_offline=True,
    project='tracking-project',
    name="tf-run",
    description="trying TraceML & Tensorflow",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

tracking.log_inputs(
    batch_size=64,
    dropout=0.2,
    learning_rate=0.001,
    optimizer="Adam"
)

# log model
estimator.train(hooks=[Callback(log_image=True, log_histo=True, log_tensor=True)])
```

### Fastai

You can log metrics, outputs, and models of Fastai experiments using the tracking module:

```python
from traceml import tracking
from traceml.integrations.fastai import Callback

tracking.init(
    is_offline=True,
    project='tracking-project',
    name="fastai-run",
    description="trying TraceML & Fastai",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

# Log model metrics
learn.fit(..., cbs=[Callback()])
```

### Pytorch Lightning

You can log metrics, outputs, and models of Pytorch Lightning experiments using the tracking module:

```python
from traceml import tracking
from traceml.integrations.pytorch_lightning import Callback

tracking.init(
    is_offline=True,
    project='tracking-project',
    name="pytorch-lightning-run",
    description="trying TraceML & Lightning",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

...
trainer = pl.Trainer(
    gpus=0,
    progress_bar_refresh_rate=20,
    max_epochs=2,
    logger=Callback(),
)
```

### HuggingFace

You can log metrics, outputs, and models of HuggingFace experiments using the tracking module:

```python
from traceml import tracking
from traceml.integrations.hugging_face import Callback

tracking.init(
    is_offline=True,
    project='tracking-project',
    name="hg-run",
    description="trying TraceML & HuggingFace",
    tags=["examples"],
    artifacts_path="path/to/artifacts/repo"
)

...
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset if training_args.do_train else None,
    eval_dataset=eval_dataset if training_args.do_eval else None,
    callbacks=[Callback],
    # ...
)
```

## Tracking artifacts

```python
import altair as alt
import matplotlib.pyplot as plt
import numpy as np
import plotly.express as px
from bokeh.plotting import figure
from vega_datasets import data

from traceml import tracking


def plot_mpl_figure(step):
    np.random.seed(19680801)
    data = np.random.randn(2, 100)

    figure, axs = plt.subplots(2, 2, figsize=(5, 5))
    axs[0, 0].hist(data[0])
    axs[1, 0].scatter(data[0], data[1])
    axs[0, 1].plot(data[0], data[1])
    axs[1, 1].hist2d(data[0], data[1])

    tracking.log_mpl_image(figure, 'mpl_image', step=step)


def log_bokeh(step):
    factors = ["a", "b", "c", "d", "e", "f", "g", "h"]
    x = [50, 40, 65, 10, 25, 37, 80, 60]

    dot = figure(title="Categorical Dot Plot", tools="", toolbar_location=None,
                 y_range=factors, x_range=[0, 100])

    dot.segment(0, factors, x, factors, line_width=2, line_color="green", )
    dot.circle(x, factors, size=15, fill_color="orange", line_color="green", line_width=3, )

    factors = ["foo 123", "bar:0.2", "baz-10"]
    x = ["foo 123", "foo 123", "foo 123", "bar:0.2", "bar:0.2", "bar:0.2", "baz-10", "baz-10",
         "baz-10"]
    y = ["foo 123", "bar:0.2", "baz-10", "foo 123", "bar:0.2", "baz-10", "foo 123", "bar:0.2",
         "baz-10"]
    colors = [
        "#0B486B", "#79BD9A", "#CFF09E",
        "#79BD9A", "#0B486B", "#79BD9A",
        "#CFF09E", "#79BD9A", "#0B486B"
    ]

    hm = figure(title="Categorical Heatmap", tools="hover", toolbar_location=None,
                x_range=factors, y_range=factors)

    hm.rect(x, y, color=colors, width=1, height=1)

    tracking.log_bokeh_chart(name='confusion-bokeh', figure=hm, step=step)


def log_altair(step):
    source = data.cars()

    brush = alt.selection(type='interval')

    points = alt.Chart(source).mark_point().encode(
        x='Horsepower:Q',
        y='Miles_per_Gallon:Q',
        color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))
    ).add_selection(
        brush
    )

    bars = alt.Chart(source).mark_bar().encode(
        y='Origin:N',
        color='Origin:N',
        x='count(Origin):Q'
    ).transform_filter(
        brush
    )

    chart = points & bars

    tracking.log_altair_chart(name='altair_chart', figure=chart, step=step)


def log_plotly(step):
    df = px.data.tips()

    fig = px.density_heatmap(df, x="total_bill", y="tip", facet_row="sex", facet_col="smoker")
    tracking.log_plotly_chart(name="2d-hist", figure=fig, step=step)


plot_mpl_figure(100)
log_bokeh(100)
log_altair(100)
log_plotly(100)
```

## Tracking DataFrames

### Summary

An extension to [pandas](http://pandas.pydata.org/) dataframes describe function.

The module contains `DataFrameSummary` object that extend `describe()` with:

- **properties**
  - dfs.columns_stats: counts, uniques, missing, missing_perc, and type per column
  - dsf.columns_types: a count of the types of columns
  - dfs[column]: more in depth summary of the column
- **function**
  - summary(): extends the `describe()` function with the values with `columns_stats`

The `DataFrameSummary` expect a pandas `DataFrame` to summarise.

```python
from traceml.summary.df import DataFrameSummary

dfs = DataFrameSummary(df)
```

getting the columns types

```python
dfs.columns_types


numeric     9
bool        3
categorical 2
unique      1
date        1
constant    1
dtype: int64
```

getting the columns stats

```python
dfs.columns_stats


                      A            B        C              D              E
counts             5802         5794     5781           5781           4617
uniques            5802            3     5771            128            121
missing               0            8       21             21           1185
missing_perc         0%        0.14%    0.36%          0.36%         20.42%
types            unique  categorical  numeric        numeric        numeric
```

getting a single column summary, e.g. numerical column

```python
# we can also access the column using numbers A[1]
dfs['A']

std                                                                 0.2827146
max                                                                  1.072792
min                                                                         0
variance                                                           0.07992753
mean                                                                0.5548516
5%                                                                  0.1603367
25%                                                                 0.3199776
50%                                                                 0.4968588
75%                                                                 0.8274732
95%                                                                  1.011255
iqr                                                                 0.5074956
kurtosis                                                            -1.208469
skewness                                                            0.2679559
sum                                                                  3207.597
mad                                                                 0.2459508
cv                                                                  0.5095319
zeros_num                                                                  11
zeros_perc                                                               0,1%
deviating_of_mean                                                          21
deviating_of_mean_perc                                                  0.36%
deviating_of_median                                                        21
deviating_of_median_perc                                                0.36%
top_correlations                         {u'D': 0.702240243124, u'E': -0.663}
counts                                                                   5781
uniques                                                                  5771
missing                                                                    21
missing_perc                                                            0.36%
types                                                                 numeric
Name: A, dtype: object
```

### [WIP] Summaries

 * [ ] Add summary analysis between columns, i.e. `dfs[[1, 2]]`

### [WIP] Visualizations

 * [ ] Add summary visualization with matplotlib.
 * [ ] Add summary visualization with plotly.
 * [ ] Add summary visualization with altair.
 * [ ] Add predefined profiling.


### [WIP] Catalog and Versions

 * [ ] Add possibility to persist summary and link to a specific version.
 * [ ] Integrate with quality libraries.



            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/polyaxon/traceml",
    "name": "traceml",
    "maintainer": "Polyaxon, Inc.",
    "docs_url": null,
    "requires_python": ">=3.8",
    "maintainer_email": "contact@polyaxon.com",
    "keywords": "polyaxon,aws,s3,microsoft,azure,google cloud storage,gcs,deep-learning,machine-learning,data-science,neural-networks,artificial-intelligence,ai,reinforcement-learning,kubernetes,aws,microsoft,azure,google cloud,tensorFlow,pytorch,matplotlib,plotly,visualization,analytics",
    "author": "Polyaxon, Inc.",
    "author_email": "contact@polyaxon.com",
    "download_url": "https://files.pythonhosted.org/packages/33/b1/404463c25627c8cebcf25050561c001b28cee922cb35f6b9a08bc295c782/traceml-1.1.1.tar.gz",
    "platform": "any",
    "description": "[![License: Apache 2](https://img.shields.io/badge/License-apache2-green.svg)](LICENSE)\n[![TraceML](https://github.com/polyaxon/traceml/actions/workflows/traceml.yml/badge.svg)](https://github.com/polyaxon/traceml/actions/workflows/traceml.yml)\n[![Slack](https://img.shields.io/badge/chat-on%20slack-aadada.svg?logo=slack&longCache=true)](https://polyaxon.com/slack/)\n[![Docs](https://img.shields.io/badge/docs-stable-brightgreen.svg?style=flat)](https://polyaxon.com/docs/)\n[![GitHub](https://img.shields.io/badge/issue_tracker-github-blue?logo=github)](https://github.com/polyaxon/polyaxon/issues)\n[![GitHub](https://img.shields.io/badge/roadmap-github-blue?logo=github)](https://github.com/polyaxon/polyaxon/milestones)\n\n<a href=\"https://polyaxon.com\"><img src=\"https://raw.githubusercontent.com/polyaxon/polyaxon/master/artifacts/packages/traceml.svg\" width=\"125\" height=\"125\" align=\"right\" /></a>\n\n# TraceML\n\nEngine for ML/Data tracking, visualization, explainability, drift detection, and dashboards for Polyaxon.\n\n## Install\n\n```bash\npip install traceml\n```\n\nIf you would like to use the tracking features, you need to install `polyaxon` as well:\n\n```bash\npip install polyaxon traceml\n```\n\n## [WIP] Local sandbox\n\n> Coming soon\n\n## Offline usage\n\nYou can enable the offline mode to track runs without an  API:\n\n```bash\nexport POLYAXON_OFFLINE=\"true\"\n```\n\nOr passing the offline flag\n\n```python\nfrom traceml import tracking\n\ntracking.init(..., is_offline=True, ...)\n```\n\n## Simple usage in a Python script\n\n```python\nimport random\n\nimport traceml as tracking\n\ntracking.init(\n    is_offline=True,\n    project='quick-start',\n    name=\"my-new-run\",\n    description=\"trying TraceML\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\n# Tracking some data refs\ntracking.log_data_ref(content=X_train, name='x_train')\ntracking.log_data_ref(content=y_train, name='y_train')\n\n# Tracking inputs\ntracking.log_inputs(\n    batch_size=64,\n    dropout=0.2,\n    learning_rate=0.001,\n    optimizer=\"Adam\"\n)\n\ndef get_loss(step):\n    result = 10 / (step + 1)\n    noise = (random.random() - 0.5) * 0.5 * result\n    return result + noise\n\n# Track metrics\nfor step in range(100):\n    loss = get_loss(step)\n    tracking.log_metrics(\n    loss=loss,\n    accuracy=(100 - loss) / 100.0,\n)\n\n# Track some one time results\ntracking.log_outputs(validation_score=0.66)\n\n# Optionally manually stop the tracking process\ntracking.stop()\n```\n\n## Integration with deep learning and machine learning libraries and frameworks\n\n### Keras\n\nYou can use TraceML's callback to automatically save all metrics and collect outputs and models, you can also track additional information using the logging methods:\n\n```python\nfrom traceml import tracking\nfrom traceml.integrations.keras import Callback\n\ntracking.init(\n    is_offline=True,\n    project='tracking-project',\n    name=\"keras-run\",\n    description=\"trying TraceML & Keras\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\ntracking.log_inputs(\n    batch_size=64,\n    dropout=0.2,\n    learning_rate=0.001,\n    optimizer=\"Adam\"\n)\ntracking.log_data_ref(content=x_train, name='x_train')\ntracking.log_data_ref(content=y_train, name='y_train')\ntracking.log_data_ref(content=x_test, name='x_test')\ntracking.log_data_ref(content=y_test, name='y_test')\n\n# ...\n\nmodel.fit(\n    x_train,\n    y_train,\n    validation_data=(X_test, y_test),\n    epochs=epochs,\n    batch_size=100,\n    callbacks=[Callback()],\n)\n```\n\n### PyTorch\n\nYou can log metrics, inputs, and outputs of Pytorch experiments using the tracking module:\n\n```python\nfrom traceml import tracking\n\ntracking.init(\n    is_offline=True,\n    project='tracking-project',\n    name=\"pytorch-run\",\n    description=\"trying TraceML & PyTorch\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\ntracking.log_inputs(\n    batch_size=64,\n    dropout=0.2,\n    learning_rate=0.001,\n    optimizer=\"Adam\"\n)\n\n# Metrics\nfor batch_idx, (data, target) in enumerate(train_loader):\n    output = model(data)\n    loss = F.nll_loss(output, target)\n    loss.backward()\n    optimizer.step()\n    tracking.log_metrics(loss=loss)\n\nasset_path = tracking.get_outputs_path('model.ckpt')\ntorch.save(model.state_dict(), asset_path)\n\n# log model\ntracking.log_artifact_ref(asset_path, framework=\"pytorch\", ...)\n```\n\n### Tensorflow\n\nYou can log metrics, outputs, and models of Tensorflow experiments and distributed Tensorflow experiments using the tracking module:\n\n```python\nfrom traceml import tracking\nfrom traceml.integrations.tensorflow import Callback\n\ntracking.init(\n    is_offline=True,\n    project='tracking-project',\n    name=\"tf-run\",\n    description=\"trying TraceML & Tensorflow\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\ntracking.log_inputs(\n    batch_size=64,\n    dropout=0.2,\n    learning_rate=0.001,\n    optimizer=\"Adam\"\n)\n\n# log model\nestimator.train(hooks=[Callback(log_image=True, log_histo=True, log_tensor=True)])\n```\n\n### Fastai\n\nYou can log metrics, outputs, and models of Fastai experiments using the tracking module:\n\n```python\nfrom traceml import tracking\nfrom traceml.integrations.fastai import Callback\n\ntracking.init(\n    is_offline=True,\n    project='tracking-project',\n    name=\"fastai-run\",\n    description=\"trying TraceML & Fastai\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\n# Log model metrics\nlearn.fit(..., cbs=[Callback()])\n```\n\n### Pytorch Lightning\n\nYou can log metrics, outputs, and models of Pytorch Lightning experiments using the tracking module:\n\n```python\nfrom traceml import tracking\nfrom traceml.integrations.pytorch_lightning import Callback\n\ntracking.init(\n    is_offline=True,\n    project='tracking-project',\n    name=\"pytorch-lightning-run\",\n    description=\"trying TraceML & Lightning\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\n...\ntrainer = pl.Trainer(\n    gpus=0,\n    progress_bar_refresh_rate=20,\n    max_epochs=2,\n    logger=Callback(),\n)\n```\n\n### HuggingFace\n\nYou can log metrics, outputs, and models of HuggingFace experiments using the tracking module:\n\n```python\nfrom traceml import tracking\nfrom traceml.integrations.hugging_face import Callback\n\ntracking.init(\n    is_offline=True,\n    project='tracking-project',\n    name=\"hg-run\",\n    description=\"trying TraceML & HuggingFace\",\n    tags=[\"examples\"],\n    artifacts_path=\"path/to/artifacts/repo\"\n)\n\n...\ntrainer = Trainer(\n    model=model,\n    args=training_args,\n    train_dataset=train_dataset if training_args.do_train else None,\n    eval_dataset=eval_dataset if training_args.do_eval else None,\n    callbacks=[Callback],\n    # ...\n)\n```\n\n## Tracking artifacts\n\n```python\nimport altair as alt\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport plotly.express as px\nfrom bokeh.plotting import figure\nfrom vega_datasets import data\n\nfrom traceml import tracking\n\n\ndef plot_mpl_figure(step):\n    np.random.seed(19680801)\n    data = np.random.randn(2, 100)\n\n    figure, axs = plt.subplots(2, 2, figsize=(5, 5))\n    axs[0, 0].hist(data[0])\n    axs[1, 0].scatter(data[0], data[1])\n    axs[0, 1].plot(data[0], data[1])\n    axs[1, 1].hist2d(data[0], data[1])\n\n    tracking.log_mpl_image(figure, 'mpl_image', step=step)\n\n\ndef log_bokeh(step):\n    factors = [\"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"]\n    x = [50, 40, 65, 10, 25, 37, 80, 60]\n\n    dot = figure(title=\"Categorical Dot Plot\", tools=\"\", toolbar_location=None,\n                 y_range=factors, x_range=[0, 100])\n\n    dot.segment(0, factors, x, factors, line_width=2, line_color=\"green\", )\n    dot.circle(x, factors, size=15, fill_color=\"orange\", line_color=\"green\", line_width=3, )\n\n    factors = [\"foo 123\", \"bar:0.2\", \"baz-10\"]\n    x = [\"foo 123\", \"foo 123\", \"foo 123\", \"bar:0.2\", \"bar:0.2\", \"bar:0.2\", \"baz-10\", \"baz-10\",\n         \"baz-10\"]\n    y = [\"foo 123\", \"bar:0.2\", \"baz-10\", \"foo 123\", \"bar:0.2\", \"baz-10\", \"foo 123\", \"bar:0.2\",\n         \"baz-10\"]\n    colors = [\n        \"#0B486B\", \"#79BD9A\", \"#CFF09E\",\n        \"#79BD9A\", \"#0B486B\", \"#79BD9A\",\n        \"#CFF09E\", \"#79BD9A\", \"#0B486B\"\n    ]\n\n    hm = figure(title=\"Categorical Heatmap\", tools=\"hover\", toolbar_location=None,\n                x_range=factors, y_range=factors)\n\n    hm.rect(x, y, color=colors, width=1, height=1)\n\n    tracking.log_bokeh_chart(name='confusion-bokeh', figure=hm, step=step)\n\n\ndef log_altair(step):\n    source = data.cars()\n\n    brush = alt.selection(type='interval')\n\n    points = alt.Chart(source).mark_point().encode(\n        x='Horsepower:Q',\n        y='Miles_per_Gallon:Q',\n        color=alt.condition(brush, 'Origin:N', alt.value('lightgray'))\n    ).add_selection(\n        brush\n    )\n\n    bars = alt.Chart(source).mark_bar().encode(\n        y='Origin:N',\n        color='Origin:N',\n        x='count(Origin):Q'\n    ).transform_filter(\n        brush\n    )\n\n    chart = points & bars\n\n    tracking.log_altair_chart(name='altair_chart', figure=chart, step=step)\n\n\ndef log_plotly(step):\n    df = px.data.tips()\n\n    fig = px.density_heatmap(df, x=\"total_bill\", y=\"tip\", facet_row=\"sex\", facet_col=\"smoker\")\n    tracking.log_plotly_chart(name=\"2d-hist\", figure=fig, step=step)\n\n\nplot_mpl_figure(100)\nlog_bokeh(100)\nlog_altair(100)\nlog_plotly(100)\n```\n\n## Tracking DataFrames\n\n### Summary\n\nAn extension to [pandas](http://pandas.pydata.org/) dataframes describe function.\n\nThe module contains `DataFrameSummary` object that extend `describe()` with:\n\n- **properties**\n  - dfs.columns_stats: counts, uniques, missing, missing_perc, and type per column\n  - dsf.columns_types: a count of the types of columns\n  - dfs[column]: more in depth summary of the column\n- **function**\n  - summary(): extends the `describe()` function with the values with `columns_stats`\n\nThe `DataFrameSummary` expect a pandas `DataFrame` to summarise.\n\n```python\nfrom traceml.summary.df import DataFrameSummary\n\ndfs = DataFrameSummary(df)\n```\n\ngetting the columns types\n\n```python\ndfs.columns_types\n\n\nnumeric     9\nbool        3\ncategorical 2\nunique      1\ndate        1\nconstant    1\ndtype: int64\n```\n\ngetting the columns stats\n\n```python\ndfs.columns_stats\n\n\n                      A            B        C              D              E\ncounts             5802         5794     5781           5781           4617\nuniques            5802            3     5771            128            121\nmissing               0            8       21             21           1185\nmissing_perc         0%        0.14%    0.36%          0.36%         20.42%\ntypes            unique  categorical  numeric        numeric        numeric\n```\n\ngetting a single column summary, e.g. numerical column\n\n```python\n# we can also access the column using numbers A[1]\ndfs['A']\n\nstd                                                                 0.2827146\nmax                                                                  1.072792\nmin                                                                         0\nvariance                                                           0.07992753\nmean                                                                0.5548516\n5%                                                                  0.1603367\n25%                                                                 0.3199776\n50%                                                                 0.4968588\n75%                                                                 0.8274732\n95%                                                                  1.011255\niqr                                                                 0.5074956\nkurtosis                                                            -1.208469\nskewness                                                            0.2679559\nsum                                                                  3207.597\nmad                                                                 0.2459508\ncv                                                                  0.5095319\nzeros_num                                                                  11\nzeros_perc                                                               0,1%\ndeviating_of_mean                                                          21\ndeviating_of_mean_perc                                                  0.36%\ndeviating_of_median                                                        21\ndeviating_of_median_perc                                                0.36%\ntop_correlations                         {u'D': 0.702240243124, u'E': -0.663}\ncounts                                                                   5781\nuniques                                                                  5771\nmissing                                                                    21\nmissing_perc                                                            0.36%\ntypes                                                                 numeric\nName: A, dtype: object\n```\n\n### [WIP] Summaries\n\n * [ ] Add summary analysis between columns, i.e. `dfs[[1, 2]]`\n\n### [WIP] Visualizations\n\n * [ ] Add summary visualization with matplotlib.\n * [ ] Add summary visualization with plotly.\n * [ ] Add summary visualization with altair.\n * [ ] Add predefined profiling.\n\n\n### [WIP] Catalog and Versions\n\n * [ ] Add possibility to persist summary and link to a specific version.\n * [ ] Integrate with quality libraries.\n\n\n",
    "bugtrack_url": null,
    "license": "Apache 2.0",
    "summary": "Engine for ML/Data tracking, visualization, dashboards, and model UI for Polyaxon.",
    "version": "1.1.1",
    "project_urls": {
        "Homepage": "https://github.com/polyaxon/traceml"
    },
    "split_keywords": [
        "polyaxon",
        "aws",
        "s3",
        "microsoft",
        "azure",
        "google cloud storage",
        "gcs",
        "deep-learning",
        "machine-learning",
        "data-science",
        "neural-networks",
        "artificial-intelligence",
        "ai",
        "reinforcement-learning",
        "kubernetes",
        "aws",
        "microsoft",
        "azure",
        "google cloud",
        "tensorflow",
        "pytorch",
        "matplotlib",
        "plotly",
        "visualization",
        "analytics"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "1bbfe358750392e2e458a40d8fa0ba29c74ad4ff6d1ad3fc8f5d1d07fb534c9b",
                "md5": "5960d6e2f5310c0fda931195eb4e91fc",
                "sha256": "a2efd88bd3449e3589e6b1763e14e923c8d728fe0069fb04664b37e04f54e49b"
            },
            "downloads": -1,
            "filename": "traceml-1.1.1-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "5960d6e2f5310c0fda931195eb4e91fc",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.8",
            "size": 133965,
            "upload_time": "2024-02-29T09:34:13",
            "upload_time_iso_8601": "2024-02-29T09:34:13.309730Z",
            "url": "https://files.pythonhosted.org/packages/1b/bf/e358750392e2e458a40d8fa0ba29c74ad4ff6d1ad3fc8f5d1d07fb534c9b/traceml-1.1.1-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "33b1404463c25627c8cebcf25050561c001b28cee922cb35f6b9a08bc295c782",
                "md5": "e197acf0ff2f8ffd909b6dff90efb121",
                "sha256": "5d179b79d03179e80e99e7cac20874290b65d7a531603fa44a660389429cd7a2"
            },
            "downloads": -1,
            "filename": "traceml-1.1.1.tar.gz",
            "has_sig": false,
            "md5_digest": "e197acf0ff2f8ffd909b6dff90efb121",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.8",
            "size": 105892,
            "upload_time": "2024-02-29T09:34:15",
            "upload_time_iso_8601": "2024-02-29T09:34:15.885349Z",
            "url": "https://files.pythonhosted.org/packages/33/b1/404463c25627c8cebcf25050561c001b28cee922cb35f6b9a08bc295c782/traceml-1.1.1.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-02-29 09:34:15",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "polyaxon",
    "github_project": "traceml",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "lcname": "traceml"
}
        
Elapsed time: 0.20830s