Python altair.Y Examples

def visualize(display_df):
        viridis = ['#440154', '#472c7a', '#3b518b', '#2c718e', '#21908d', '#27ad81', '#5cc863', '#aadc32', '#fde725']
        import altair as alt
        color_scale = alt.Scale(
            domain=(display_df.dropna().trending.min(),
                    0,
                    display_df.dropna().trending.max()),
            range=[viridis[0], viridis[len(viridis) // 2], viridis[-1]]
        )

        return alt.Chart(display_df).mark_circle().encode(
            alt.X('variable'),
            alt.Y('term'),
            size='frequency',
            color=alt.Color('trending:Q', scale=color_scale),
        ) 

def altair_step_matrix(diff, plot_name=None, title='', vmin=None, vmax=None, font_size=12, **kwargs):
    heatmap_data = diff.reset_index().melt('index')
    heatmap_data.columns = ['y', 'x', 'z']
    table = alt.Chart(heatmap_data).encode(
        x=alt.X('x:O', sort=None),
        y=alt.Y('y:O', sort=None)
    )
    heatmap = table.mark_rect().encode(
        color=alt.Color(
            'z:Q',
            scale=alt.Scale(scheme='blues'),
        )
    )
    text = table.mark_text(
        align='center', fontSize=font_size
    ).encode(
        text='z',
        color=alt.condition(
            abs(alt.datum.z) < 0.8,
            alt.value('black'),
            alt.value('white'))
    )
    heatmap_object = (heatmap + text).properties(
        width=3 * font_size * len(diff.columns),
        height=2 * font_size * diff.shape[0]
    )
    return heatmap_object, plot_name, None, diff.retention.retention_config 

def hist(self, bins=None, orientation="vertical", **kwargs):
        data = self._preprocess_data(with_index=False)
        column = data.columns[0]
        if isinstance(bins, int):
            bins = alt.Bin(maxbins=bins)
        elif bins is None:
            bins = True
        if orientation == "vertical":
            Indep, Dep = alt.X, alt.Y
        elif orientation == "horizontal":
            Indep, Dep = alt.Y, alt.X
        else:
            raise ValueError("orientation must be 'horizontal' or 'vertical'.")

        mark = self._get_mark_def({"type": "bar", "orient": orientation}, kwargs)
        return alt.Chart(data, mark=mark).encode(
            Indep(column, title=None, bin=bins), Dep("count()", title="Frequency")
        ) 

def pairplot(data, hue=None, vars=None):
    if vars is None:
        vars = list(data.columns)

    chart = alt.Chart(data).mark_circle().encode(
                alt.X(alt.repeat("column"), type='quantitative'),
                alt.Y(alt.repeat("row"), type='quantitative'),
                color='{hue}:N'.format(hue=hue)
            ).properties(
                width=250,
                height=250
            ).repeat(
                row=vars,
                column=vars
            )
    return chart 

def scatterplot(x, y, data, hue=None, xlim=None, ylim=None):
    # TODO: refactor so it uses category_chart_kwargs?
    if xlim is None:
        xlim = get_limit_tuple(data[x])
    if ylim is None:
        ylim = get_limit_tuple(data[y])
    xscale = alt.Scale(domain=xlim)
    yscale = alt.Scale(domain=ylim)
    
    other_args = {'color': '{hue}:N'.format(hue=hue)} if hue else {}
    points = alt.Chart(data).mark_circle().encode(
        alt.X(x, scale=xscale),
        alt.Y(y, scale=yscale),
        **other_args
    )
    return points 

def frame_selector_ui(summary):
    st.sidebar.markdown("# Frame")

    # The user can pick which type of object to search for.
    object_type = st.sidebar.selectbox("Search for which objects?", summary.columns, 2)

    # The user can select a range for how many of the selected objecgt should be present.
    min_elts, max_elts = st.sidebar.slider("How many %ss (select a range)?" % object_type, 0, 25, [10, 20])
    selected_frames = get_selected_frames(summary, object_type, min_elts, max_elts)
    if len(selected_frames) < 1:
        return None, None

    # Choose a frame out of the selected frames.
    selected_frame_index = st.sidebar.slider("Choose a frame (index)", 0, len(selected_frames) - 1, 0)

    # Draw an altair chart in the sidebar with information on the frame.
    objects_per_frame = summary.loc[selected_frames, object_type].reset_index(drop=True).reset_index()
    chart = alt.Chart(objects_per_frame, height=120).mark_area().encode(
        alt.X("index:Q", scale=alt.Scale(nice=False)),
        alt.Y("%s:Q" % object_type))
    selected_frame_df = pd.DataFrame({"selected_frame": [selected_frame_index]})
    vline = alt.Chart(selected_frame_df).mark_rule(color="red").encode(
        alt.X("selected_frame:Q", axis=None)
    )
    st.sidebar.altair_chart(alt.layer(chart, vline))

    selected_frame = selected_frames[selected_frame_index]
    return selected_frame_index, selected_frame

# Select frames based on the selection in the sidebar 

def hist_frame(self, column=None, layout=(-1, 2), **kwargs):
        if column is not None:
            if isinstance(column, str):
                column = [column]
        data = self._preprocess_data(with_index=False, usecols=column)
        data = data._get_numeric_data()
        nrows, ncols = _get_layout(data.shape[1], layout)
        return (
            alt.Chart(data, mark=self._get_mark_def("bar", kwargs))
            .encode(
                x=alt.X(alt.repeat("repeat"), type="quantitative", bin=True),
                y=alt.Y("count()", title="Frequency"),
            )
            .repeat(repeat=list(data.columns), columns=ncols)
        ) 

def hist(self, bins=None, stacked=None, orientation="vertical", **kwargs):
        data = self._preprocess_data(with_index=False)
        if isinstance(bins, int):
            bins = alt.Bin(maxbins=bins)
        elif bins is None:
            bins = True
        if orientation == "vertical":
            Indep, Dep = alt.X, alt.Y
        elif orientation == "horizontal":
            Indep, Dep = alt.Y, alt.X
        else:
            raise ValueError("orientation must be 'horizontal' or 'vertical'.")

        mark = self._get_mark_def({"type": "bar", "orient": orientation}, kwargs)
        chart = (
            alt.Chart(data, mark=mark)
            .transform_fold(list(data.columns), as_=["column", "value"])
            .encode(
                Indep("value:Q", title=None, bin=bins),
                Dep("count()", title="Frequency", stack=stacked),
                color="column:N",
            )
        )

        if kwargs.get("subplots"):
            nrows, ncols = _get_layout(data.shape[1], kwargs.get("layout", (-1, 1)))
            chart = chart.encode(facet=alt.Facet("column:N", title=None)).properties(
                columns=ncols
            )

        return chart 

def _xy(self, mark, x=None, y=None, stacked=False, subplots=False, **kwargs):
        data = self._preprocess_data(with_index=True)

        if x is None:
            x = data.columns[0]
        else:
            x = _valid_column(x)
            assert x in data.columns

        if y is None:
            y_values = list(data.columns[1:])
        else:
            y = _valid_column(y)
            assert y in data.columns
            y_values = [y]

        chart = (
            alt.Chart(data, mark=self._get_mark_def(mark, kwargs))
            .transform_fold(y_values, as_=["column", "value"])
            .encode(
                x=x,
                y=alt.Y("value:Q", title=None, stack=stacked),
                color=alt.Color("column:N", title=None),
                tooltip=[x] + y_values,
            )
            .interactive()
        )

        if subplots:
            nrows, ncols = _get_layout(len(y_values), kwargs.get("layout", (-1, 1)))
            chart = chart.encode(facet=alt.Facet("column:N", title=None)).properties(
                columns=ncols
            )

        return chart 

def _xy(self, mark, **kwargs):
        data = self._preprocess_data(with_index=True)
        return (
            alt.Chart(data, mark=self._get_mark_def(mark, kwargs))
            .encode(
                x=alt.X(data.columns[0], title=None),
                y=alt.Y(data.columns[1], title=None),
                tooltip=list(data.columns),
            )
            .interactive()
        ) 

def heatmap(data, vmin=None, vmax=None, annot=None, fmt='.2g'):

    # We always want to have a DataFrame with semantic information
    if not isinstance(data, pd.DataFrame):
        matrix = np.asarray(data)
        data = pd.DataFrame(matrix)

    melted = data.stack().reset_index(name='Value')

    x = data.columns.name
    y = data.index.name

    heatmap = alt.Chart(melted).mark_rect().encode(
        alt.X('{x}:O'.format(x=x), scale=alt.Scale(paddingInner=0)),
        alt.Y('{y}:O'.format(y=y), scale=alt.Scale(paddingInner=0)),
        color='Value:Q'
    )
    
    if not annot:
        return heatmap

    # Overlay text
    text = alt.Chart(melted).mark_text(baseline='middle').encode(
        x='{x}:O'.format(x=x),
        y='{y}:O'.format(y=y),
        text=alt.Text('Value', format=fmt),
        color=alt.condition(alt.expr.datum['Value'] > 70,
                            alt.value('black'),
                            alt.value('white'))
    )
    return heatmap + text 

def jointplot(x, y, data, kind='scatter', hue=None, xlim=None, ylim=None):
    if xlim is None:
        xlim = get_limit_tuple(data[x])
    if ylim is None:
        ylim = get_limit_tuple(data[y])
    xscale = alt.Scale(domain=xlim)
    yscale = alt.Scale(domain=ylim)
 
    points = scatterplot(x, y, data, hue=hue, xlim=xlim, ylim=ylim)

    area_args = {'opacity': .3, 'interpolate': 'step'}

    blank_axis = alt.Axis(title='')

    top_hist = alt.Chart(data).mark_area(**area_args).encode(
        alt.X('{x}:Q'.format(x=x),
              # when using bins, the axis scale is set through
              # the bin extent, so we do not specify the scale here
              # (which would be ignored anyway)
              bin=alt.Bin(maxbins=20, extent=xscale.domain),
              stack=None,
              axis=blank_axis,
             ),
        alt.Y('count()', stack=None, axis=blank_axis),
        alt.Color('{hue}:N'.format(hue=hue)),
    ).properties(height=60)

    right_hist = alt.Chart(data).mark_area(**area_args).encode(
        alt.Y('{y}:Q'.format(y=y),
              bin=alt.Bin(maxbins=20, extent=yscale.domain),
              stack=None,
              axis=blank_axis,
             ),
        alt.X('count()', stack=None, axis=blank_axis),
        alt.Color('{hue}:N'.format(hue=hue)),
    ).properties(width=60)

    return top_hist & (points | right_hist) 

def airport_chart(source: alt.Chart, subset: List[str], name: str) -> alt.Chart:

    chart = source.transform_filter(
        alt.FieldOneOfPredicate(field="airport", oneOf=subset)
    )

    highlight = alt.selection(
        type="single", nearest=True, on="mouseover", fields=["airport"]
    )

    points = (
        chart.mark_point()
        .encode(
            x="day",
            y=alt.Y("count", title="# of departing flights"),
            color=alt.Color("airport", legend=alt.Legend(title=name)),
            tooltip=["day", "airport", "city", "count"],
            opacity=alt.value(0.3),
        )
        .add_selection(highlight)
    )

    lines = (
        chart.mark_line()
        .encode(
            x="day",
            y="count",
            color="airport",
            size=alt.condition(~highlight, alt.value(1), alt.value(3)),
        )
        .transform_loess("day", "count", groupby=["airport"], bandwidth=0.2)
    )

    return lines + points 

def airline_chart(
    source: alt.Chart, subset: List[str], name: str, loess=True
) -> alt.Chart:

    chart = source.transform_filter(
        alt.FieldOneOfPredicate(field="airline", oneOf=subset)
    )

    highlight = alt.selection(
        type="single", nearest=True, on="mouseover", fields=["airline"]
    )

    points = (
        chart.mark_point()
        .encode(
            x="day",
            y=alt.Y("rate", title="# of flights (normalized)"),
            color=alt.Color("airline", legend=alt.Legend(title=name)),
            tooltip=["day", "airline", "count"],
            opacity=alt.value(0.3),
        )
        .add_selection(highlight)
    )

    lines = chart.mark_line().encode(
        x="day",
        y="rate",
        color="airline",
        size=alt.condition(~highlight, alt.value(1), alt.value(3)),
    )
    if loess:
        lines = lines.transform_loess(
            "day", "rate", groupby=["airline"], bandwidth=0.2
        )

    return lines + points 

def st_heatmap(
    heatmap_df: pd.DataFrame, x_col_name: str, y_col_name: str, color_col_name: str
):
    heatmap = (
        alt.Chart(heatmap_df, height=700, width=700)
        .mark_rect()
        .encode(alt.X(x_col_name), alt.Y(y_col_name), alt.Color(color_col_name))
    )
    st.altair_chart(heatmap) 

def show_document_length_distribution(tokens: List[List[str]]):
    st.header("Document Length Distribution")
    document_lengths = get_document_lengths(tokens)
    doc_lengths = pd.DataFrame({"Token Count": document_lengths})
    doc_length_chart = (
        alt.Chart(doc_lengths, height=500, width=700)
        .mark_bar()
        .encode(
            alt.X("Token Count", bin=alt.Bin(maxbins=30)),
            alt.Y("count()", type="quantitative"),
        )
    )

    st.altair_chart(doc_length_chart) 

def _y(y, df, ordinal_threshold=6, **kwargs):
    return alt.Y(
        field=y,
        type=infer_vegalite_type(df[y], ordinal_threshold=ordinal_threshold),
        **kwargs
    ) 

def visualize_delta_confidence_per_model(self, 
        instance_hash: Dict[InstanceKey, Instance]={},
        instance_hash_rewritten: Dict[InstanceKey, Instance]={},
        filtered_instances: List[InstanceKey]=None,
        model: str=None):
        """
        Visualize the rewrite distribution, in terms of model confidence. 
        It's a histogram that shows the distribution of the delta confidence.
        This historgram is different
        for each different model. 
        
        Parameters
        ----------
        instance_hash : Dict[InstanceKey, Instance]
            A dict that saves all the *original* instances, by default {}. 
            It denotes by the corresponding instance keys.
            If ``{}``, resolve to ``Instance.instance_hash``.
        instance_hash_rewritten : Dict[InstanceKey, Instance]
            A dict that saves all the *rewritten* instances, by default {}. 
            It denotes by the corresponding instance keys.
            If ``{}``, resolve to ``Instance.instance_hash_rewritten``.
        filtered_instances : List[InstanceKey], optional
            A selected list of instances. If given, only display the distribution
            of the selected instances, by default None
        model : str, optional
            The selected model, by default ``None``. 
            If ``None``, resolve to ``Instance.model``.
        
        Returns
        -------
        alt.Chart
            An altair chart object. 
        """
        model = Instance.resolve_default_model(model)
        instance_hash = instance_hash or Instance.instance_hash
        instance_hash_rewritten = instance_hash_rewritten or Instance.instance_hash_rewritten
        output = []
        if filtered_instances:
            qids = list(np.unique([i.qid for i in filtered_instances]))
        else:
            qids = None
        data = Rewrite.get_delta_performance(self,
            qids, instance_hash, instance_hash_rewritten, model)['delta_confidences']
        output = [ {"delta_confidence": d} for d in data ] 
        df = pd.DataFrame(output)
        chart = alt.Chart(df).mark_bar().encode(
            y=alt.Y('count()'),
            x=alt.X('delta_confidence:Q', bin=True)
        ).properties(width=150, height=100, title=f'{self.rid} on {model}')#.configure_facet(spacing=5)#
        return chart 

def visualize_models(self, 
        instance_hash: Dict[InstanceKey, Instance]={},
        instance_hash_rewritten: Dict[InstanceKey, Instance]={},
        filtered_instances: List[InstanceKey]=None,
        models: str=[]):
        """
        Visualize the rewrite distribution. 
        It's a one-bar histogram that displays the count of instances rewritten, and
        the proportion of "flip_to_correct", "flip_to_incorrect", "unflip"
        Because of the flipping proportion, this historgram is different
        for each different model. 
        
        Parameters
        ----------
        instance_hash : Dict[InstanceKey, Instance]
            A dict that saves all the *original* instances, by default {}. 
            It denotes by the corresponding instance keys.
            If ``{}``, resolve to ``Instance.instance_hash``.
        instance_hash_rewritten : Dict[InstanceKey, Instance]
            A dict that saves all the *rewritten* instances, by default {}. 
            It denotes by the corresponding instance keys.
            If ``{}``, resolve to ``Instance.instance_hash_rewritten``.
        filtered_instances : List[InstanceKey], optional
            A selected list of instances. If given, only display the distribution
            of the selected instances, by default None
        models : List[str], optional
            A list of instances, with the bars for each group concated vertically.
            By default []. If [], resolve to ``[ Instance.model ]``.
        
        Returns
        -------
        alt.Chart
            An altair chart object. 
        """
        model = models or [ Instance.model ]
        instance_hash = instance_hash or Instance.instance_hash
        instance_hash_rewritten = instance_hash_rewritten or Instance.instance_hash_rewritten
        if not models:
            models = [ Instance.resolve_default_model(None) ]
        output = []
        for model in models:
            #Instance.set_default_model(model=model)
            data = self.serialize(instance_hash, instance_hash_rewritten, filtered_instances, model)
            for flip, count in data["counts"].items():
                output.append({
                    "flip": flip,
                    "count": count,
                    "model": model
                })
        df = pd.DataFrame(output)
        chart = alt.Chart(df).mark_bar().encode(
            y=alt.Y('model:N'),
            x=alt.X('count:Q', stack="zero"),
            color=alt.Color('flip:N', scale=alt.Scale(
                range=["#1f77b4", "#ff7f0e", "#c7c7c7"],
                domain=["flip_to_correct", "flip_to_incorrect", "unflip"])),
            tooltip=['model:N', 'count:Q', 'correctness:N']
        ).properties(width=100)#.configure_facet(spacing=5)#
        return chart 

def visualize_models(self, 
        instance_hash: Dict[InstanceKey, Instance]={},
        instance_hash_rewritten: Dict[InstanceKey, Instance]={},
        filtered_instances: List[InstanceKey]=None,
        models: List[str]=[]):
        """
        Visualize the group distribution. 
        It's a one-bar histogram that displays the count of instances in the group, and
        the proportion of incorrect predictions.
        Because of the incorrect prediction proportion, this historgram is different
        for each different model. 
        
        Parameters
        ----------
        instance_hash : Dict[InstanceKey, Instance]
            A dict that saves all the *original* instances, by default {}. 
            It denotes by the corresponding instance keys.
            If ``{}``, resolve to ``Instance.instance_hash``.
        instance_hash_rewritten : Dict[InstanceKey, Instance]
            A dict that saves all the *rewritten* instances, by default {}. 
            It denotes by the corresponding instance keys.
            If ``{}``, resolve to ``Instance.instance_hash_rewritten``.
        filtered_instances : List[InstanceKey], optional
            A selected list of instances. If given, only display the distribution
            of the selected instances, by default None
        models : List[str], optional
            A list of instances, with the bars for each group concated vertically.
            By default []. If [], resolve to ``[ Instance.model ]``.
        
        Returns
        -------
        alt.Chart
            An altair chart object. 
        """
        instance_hash = instance_hash or Instance.instance_hash
        instance_hash_rewritten = instance_hash_rewritten or Instance.instance_hash_rewritten
        models = models or [ Instance.resolve_default_model(None) ]
        output = []
        for model in models:
            #Instance.set_default_model(model=model)
            data = self.serialize(instance_hash, instance_hash_rewritten, filtered_instances, model)
            for correctness, count in data["counts"].items():
                output.append({
                    "correctness": correctness,
                    "count": count,
                    "model": model
                })
        
        df = pd.DataFrame(output)
        chart = alt.Chart(df).mark_bar().encode(
            y=alt.Y('model:N'),
            x=alt.X('count:Q', stack="zero"),
            color=alt.Color('correctness:N', scale=alt.Scale(domain=["correct", "incorrect"])),
            tooltip=['model:N', 'count:Q', 'correctness:N']
        ).properties(width=100)#.configure_facet(spacing=5)#
        return chart 

def boxplot_vertical(x=None, y=None, hue=None, data=None, order=None):

    # orientation_mapper = {'v': {'x': 'x', 'y': 'y'},
    #                       'h': {'x': 'y', 'y': 'x'}}

    # Define aggregate fields
    lower_box = 'q1({value}):Q'.format(value=y)
    lower_whisker = 'min({value}):Q'.format(value=y)
    upper_box = 'q3({value}):Q'.format(value=y)
    upper_whisker = 'max({value}):Q'.format(value=y)
    
    kwargs = {'x': '{x}:O'.format(x=x)}

    if hue is not None:
        kwargs['color'] = '{hue}:N'.format(hue=hue)
        # Swap x for column
        column, kwargs['x'] = kwargs['x'], '{hue}:N'.format(hue=hue)

    base = alt.Chart().encode(
        **kwargs
    )

    # Compose each layer individually
    lower_whisker = base.mark_rule().encode(
        y=alt.Y(lower_whisker, axis=alt.Axis(title=y)),
        y2=lower_box,
    )
    
    middle_bar_kwargs = dict(
        y=lower_box,
        y2=upper_box,
    )
    if hue is None:
        middle_bar_kwargs['color'] = 'year:O'

    middle_bar = base.mark_bar(size=10.0).encode(**middle_bar_kwargs)

    upper_whisker = base.mark_rule().encode(
        y=upper_whisker,
        y2=upper_box,
    )
    
    middle_tick = base.mark_tick(
        color='white',
        size=10.0
    ).encode(
        y='median({value}):Q'.format(value=y),
    )
    
    chart = (lower_whisker + upper_whisker + middle_bar + middle_tick)

    if hue is None:
        chart.data = data
        return chart
    else:
        return chart.facet(column=column, data=data) 

def show_label_distribution(
    sample_labels: Union[List[str], List[List[str]]],
    all_labels: Optional[Union[List[str], List[List[str]]]] = None,
):
    if sample_labels is not None:
        st.header("Label Distribution")
        label_counts = _collect_label_counts(sample_labels)

        if all_labels is None:
            label_chart = (
                alt.Chart(label_counts, height=500, width=700)
                .mark_bar()
                .encode(
                    alt.X("Label", type="nominal"),
                    alt.Y("Proportion", type="quantitative"),
                )
            )
        else:
            label_counts["Label Set"] = "Sample"
            all_label_counts = _collect_label_counts(all_labels)
            all_label_counts["Label Set"] = "All Documents"
            label_counts = pd.concat([label_counts, all_label_counts])

            label_chart = (
                alt.Chart(label_counts, width=100)
                .mark_bar()
                .encode(
                    alt.X(
                        "Label Set",
                        type="nominal",
                        title=None,
                        sort=["Sample", "All Documents"],
                    ),
                    alt.Y("Proportion", type="quantitative"),
                    alt.Column(
                        "Label", type="nominal", header=alt.Header(labelAngle=0)
                    ),
                    alt.Color("Label Set", type="nominal", legend=None),
                )
            )

        st.altair_chart(label_chart)