More test import speedups (~4x for CLI commands w/ models)

onecodex/distance.py

@@ -1,5 +1,4 @@
 import pandas as pd
-import skbio.diversity
 
 from onecodex.exceptions import OneCodexException
 from onecodex.taxonomy import TaxonomyMixin
@@ -20,6 +19,8 @@ def alpha_diversity(self, metric="simpson", rank="auto"):
         -------
         pandas.DataFrame, a distance matrix.
         """
+        import skbio.diversity
+
         if metric not in ("simpson", "chao1", "shannon"):
             raise OneCodexException(
                 "For alpha diversity, metric must be one of: simpson, chao1, shannon"
@@ -55,6 +56,8 @@ def beta_diversity(self, metric="braycurtis", rank="auto"):
         -------
         skbio.stats.distance.DistanceMatrix, a distance matrix.
         """
+        import skbio.diversity
+
         if metric not in ("jaccard", "braycurtis", "cityblock"):
             raise OneCodexException(
                 "For beta diversity, metric must be one of: jaccard, braycurtis, cityblock"
@@ -88,6 +91,8 @@ def unifrac(self, weighted=True, rank="auto"):
         skbio.stats.distance.DistanceMatrix, a distance matrix.
         """
         # needs read counts, not relative abundances
+        import skbio.diversity
+
         if self._guess_normalized():
             raise OneCodexException("UniFrac requires unnormalized read counts.")
 

onecodex/viz/_bargraph.py

@@ -1,4 +1,3 @@
-import altair as alt
 from onecodex.exceptions import OneCodexException
 
 
@@ -65,6 +64,9 @@ def plot_bargraph(
 
         >>> plot_bargraph(rank='genus', top_n=10)
         """
+        # Deferred imports
+        import altair as alt
+
         if rank is None:
             raise OneCodexException("Please specify a rank or 'auto' to choose automatically")
 

onecodex/viz/_distance.py

@@ -1,19 +1,9 @@
 # -*- coding: utf-8 -*-
-import altair as alt
 from itertools import chain
-import numpy as np
-import pandas as pd
-from scipy.cluster import hierarchy
-from scipy.spatial.distance import squareform
-from scipy.stats import pearsonr
-from skbio.stats import ordination
-from sklearn import manifold
-from sklearn.metrics.pairwise import euclidean_distances
 import warnings
 
 from onecodex.exceptions import OneCodexException
 from onecodex.distance import DistanceMixin
-from onecodex.viz import dendrogram
 
 
 class VizDistanceMixin(DistanceMixin):
@@ -43,6 +33,10 @@ def _compute_distance(self, rank, metric):
         return distances
 
     def _cluster_by_sample(self, rank="auto", metric="braycurtis", linkage="average"):
+        from scipy.cluster import hierarchy
+        from scipy.spatial.distance import squareform
+        from sklearn.metrics.pairwise import euclidean_distances
+
         if metric == "euclidean":
             dist_matrix = euclidean_distances(self._results).round(6)
         else:
@@ -59,6 +53,10 @@ def _cluster_by_sample(self, rank="auto", metric="braycurtis", linkage="average"
         }
 
     def _cluster_by_taxa(self, linkage="average"):
+        from scipy.cluster import hierarchy
+        from scipy.spatial.distance import squareform
+        from sklearn.metrics.pairwise import euclidean_distances
+
         dist_matrix = euclidean_distances(self._results.T).round(6)
         clustering = hierarchy.linkage(squareform(dist_matrix), method=linkage)
         scipy_tree = hierarchy.dendrogram(clustering, no_plot=True)
@@ -116,6 +114,11 @@ def plot_distance(
 
         >>> plot_distance(rank='genus', metric='unifrac')
         """
+        import altair as alt
+        import numpy as np
+        import pandas as pd
+        from onecodex.viz import dendrogram
+
         if len(self._results) < 2:
             raise OneCodexException(
                 "`plot_distance` requires 2 or more valid classification results."
@@ -263,6 +266,15 @@ def plot_mds(
         they truly represent the calculated distances. They do not reflect how well the distance
         metric captures similarities between the underlying data (in this case, an OTU table).
         """
+        import altair as alt
+        import numpy as np
+        import pandas as pd
+        from scipy.spatial.distance import squareform
+        from scipy.stats import pearsonr
+        from skbio.stats import ordination
+        from sklearn import manifold
+        from sklearn.metrics.pairwise import euclidean_distances
+
         if len(self._results) < 2:
             raise OneCodexException("`plot_mds` requires 2 or more valid classification results.")
 

onecodex/viz/_heatmap.py

@@ -1,6 +1,3 @@
-import altair as alt
-import pandas as pd
-
 from onecodex.exceptions import OneCodexException
 
 
@@ -77,6 +74,10 @@ def plot_heatmap(
 
         >>> plot_heatmap(rank='family', top_n=10)
         """
+        # Deferred imports
+        import altair as alt
+        import pandas as pd
+
         if rank is None:
             raise OneCodexException("Please specify a rank or 'auto' to choose automatically")
 

onecodex/viz/_metadata.py

@@ -1,8 +1,4 @@
-import altair as alt
-import pandas as pd
-
 from onecodex.exceptions import OneCodexException
-from onecodex.viz import boxplot
 
 
 class VizMetadataMixin(object):
@@ -67,6 +63,11 @@ def plot_metadata(
 
         >>> plot_metadata(haxis=('allergy_dogs', 'allergy_cats'), vaxis='Bacteroides')
         """
+        # Deferred imports
+        import altair as alt
+        import pandas as pd
+        from onecodex.viz import boxplot
+
         if rank is None:
             raise OneCodexException("Please specify a rank or 'auto' to choose automatically")
 

onecodex/viz/_pca.py

@@ -1,8 +1,3 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-from sklearn.decomposition import PCA
-
 from onecodex.exceptions import OneCodexException
 
 
@@ -72,6 +67,12 @@ def plot_pca(
 
         >>> plot_pca(tooltip=['Bacteroides', 'Prevotella', 'Bifidobacterium'])
         """
+        # Deferred imports
+        import altair as alt
+        import numpy as np
+        import pandas as pd
+        from sklearn.decomposition import PCA
+
         if rank is None:
             raise OneCodexException("Please specify a rank or 'auto' to choose automatically")