From 82e7f59e06a3b13df64390ba87e89c65946bd373 Mon Sep 17 00:00:00 2001
From: mribary <m.ribary@surrey.ac.uk>
Date: Wed, 22 Apr 2020 18:21:16 +0100
Subject: [PATCH] Experiments with hierarchical clustering
---
pyDigest.py | 96 +++++++++++++++++++-
pyDigest_documentation.md | 38 +++++++-
script/NLP_clustering_sections.ipynb | 130 ++++++++++++++-------------
script/avg_euc_clusters.png | Bin 0 -> 363129 bytes
script/ward_euc_clusters.png | Bin 0 -> 366170 bytes
5 files changed, 201 insertions(+), 63 deletions(-)
create mode 100644 script/avg_euc_clusters.png
create mode 100644 script/ward_euc_clusters.png
diff --git a/pyDigest.py b/pyDigest.py
index eb53c4a..9631d21 100644
--- a/pyDigest.py
+++ b/pyDigest.py
@@ -60,4 +60,98 @@ def similar_sections(id, size=10):
title = doc_df.Title[id]
print("Thematic sections most similar to thematic section %r:" %id)
print("%r" %title)
- return similar_df_id
\ No newline at end of file
+ return similar_df_id
+
+def linkage_for_clustering(X, threshold=0.5):
+ ''' The function takes a matrix X with observations stored in rows and features stored in columns.
+ It returns a dataframe with linkage combinations of method and metric used for hierarchical
+ clustering sorted by reverse order based on the absolute value of the cophenetic correlation
+ coefficient (CCC). The CCC score ranges between -1 and 1 and measures how how faithfully a
+ dendrogram preserves the pairwise distances between the original unmodeled data points.
+ The cophenetic correlation is expected to be positive if the original distances are compared
+ to cophenetic distances (or similarities to similarities) and negative if distances are
+ compared to similarities.
+ It needs to be noted that CCC is calculated for the whole dendrogram. Ideally, one should
+ calculate CCC at the specific cut point where the dendrogram's output is used to identify the
+ clusters. It is recommended to calculate CCC at the specific cut level yielding k clusters to
+ confirm that the correct method-metric combination has been used for hierarchical clustering.
+ The 'average' method generally produces the best CCC score especially with matrices with high
+ dimensionality. Instead of relying exclusively on the CCC score, one also needs to consider
+ what method-metric combination suits the particular dataset on which hierarchical clustering
+ is performed by scipy's linkage function.
+ '''
+ import numpy as np
+ # Handle errors
+ if isinstance(X, np.ndarray) is not True:
+ raise TypeError("X must be a matrix with samples in rows and observations in columns")
+ if type(threshold) != float:
+ raise TypeError("threshold must be a float")
+ if abs(threshold) > 1:
+ raise ValueError("threshold must be between -1 and 1")
+ # Import basic packages
+ import pandas as pd
+ from scipy.cluster.hierarchy import linkage
+ # List of 7 methods for the linkage function
+ methods = ['ward', 'single', 'complete', 'average', 'weighted', 'centroid', 'median']
+ # List of 22 metrics for the linkage function
+ metrics = ['braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', \
+ 'dice', 'euclidean', 'hamming', 'jaccard', 'jensenshannon', 'kulsinski', 'mahalanobis', \
+ 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', \
+ 'sokalsneath', 'sqeuclidean', 'yule']
+ # Create list of dictioanries for the 154 method-metric combinations
+ dicts = []
+ for x in methods:
+ for y in metrics:
+ d = {'method':x, 'metric':y}
+ dicts.append(d)
+ # Load combinations into a dataframe
+ linkages = {'method':[], 'metric': []}
+ for i in range(len(dicts)):
+ linkages['method'].append(dicts[i]['method'])
+ linkages['metric'].append(dicts[i]['metric'])
+ l = pd.DataFrame(linkages, columns=['method', 'metric'])
+ # Calculate linkage matrices (Z) from X
+ Z_matrices = []
+ valid_mms = []
+ count = 0
+ for i in range(len(dicts)):
+ try:
+ Z = linkage(X, method=dicts[i]['method'], metric=dicts[i]['metric'])
+ Z_matrices.append(Z)
+ valid_mms.append(True)
+ print(str(count) + ' ' + str(dicts[i]['method']) + ' & ' + (dicts[i]['metric']) + ' - matrix ready')
+ count = count + 1
+ except:
+ valid_mms.append(False)
+ pass
+ # Drop invald combinations and reindex
+ l = l.loc[valid_mms]
+ l.reset_index(drop=True)
+ # Calculate Cophenetic Correlation Coefficient for valid linkage combinations
+ from scipy.cluster.hierarchy import cophenet
+ from scipy.spatial.distance import pdist
+ valid_scores = []
+ CCC_scores = []
+ for Z in Z_matrices:
+ try:
+ c, coph_dists = cophenet(Z, pdist(X))
+ if np.isnan(c):
+ valid_scores.append(False)
+ CCC_scores.append(None)
+ print('no score')
+ else:
+ valid_scores.append(True)
+ CCC_scores.append(c)
+ print(c)
+ except RuntimeWarning:
+ valid_scores.append(False)
+ print('no score')
+ pass
+ # Insert scores, drop no values and reset index
+ l['CCC_score'] = CCC_scores
+ l['CCC_abs_score'] = [abs(number) if number is not None else number for number in CCC_scores]
+ l = l.loc[valid_scores]
+ l.reset_index(drop=True)
+ # Sort method-metric pairs according to CCC score
+ l.sort_values(by=['CCC_score', 'method', 'metric'], ascending=False, inplace=True)
+ return l[l.CCC_score > threshold]
\ No newline at end of file
diff --git a/pyDigest_documentation.md b/pyDigest_documentation.md
index df73c7b..6a3ca70 100644
--- a/pyDigest_documentation.md
+++ b/pyDigest_documentation.md
@@ -50,4 +50,40 @@ The function returns a dataframe with the most similar thematic sections to the
`size`: the number of documents returned, default value is set to 10.
-The function is used, for example, in `NLP_sections_002.py` to identify the most similar items among the _Digest_'s 432 thematic sections.
\ No newline at end of file
+The function is used, for example, in `NLP_sections_002.py` to identify the most similar items among the _Digest_'s 432 thematic sections.
+
+### 3. `linkage_for_clustering(X, threshold=0.5)`
+
+The function takes a matrix X with observations stored in rows and features stored in columns. It returns a dataframe with linkage combinations of method and metric used for hierarchical clustering sorted by reverse order based on the absolute value of the cophenetic correlation coefficient (CCC). The CCC score ranges between -1 and 1 and measures how how faithfully a dendrogram preserves the pairwise distances between the original unmodeled data points. The cophenetic correlation is expected to be positive if the original distances are compared to cophenetic distances (or similarities to similarities) and negative if distances are compared to similarities.
+
+It needs to be noted that CCC is calculated for the whole dendrogram. Ideally, one should calculate CCC at the specific cut point where the dendrogram's output is used to identify the clusters. It is recommended to calculate CCC at the specific cut level yielding k clusters to confirm that the correct method-metric combination has been used for hierarchical clustering.
+
+The 'average' method generally produces the best CCC score especially with matrices with high dimensionality. Instead of relying exclusively on the CCC score, one also needs to consider what method-metric combination suits the particular dataset on which hierarchical clustering is performed by scipy's linkage function.
+
+#### Methods for scipy's linkage function
+
+Default method is `'ward'`.
+
+| Method | Note |
+|:---|:---|
+| 'ward' | Uses the Ward variance minimization algorithm |
+| 'single' | Nearest Point algorithm |
+| 'complete' | Farthest Point Algorithm or Voor Hees Algorithm |
+| 'average' | UPGMA algorithm |
+| 'weighted'| WPGMA algorithm |
+| 'centroid' | Euclidean distance between the centroid and the centroid of a remaining cluster |
+| 'median' | Merged clusters' centroid to be come the average |
+
+#### Metrics for scipy's linkage function
+
+Default metric is `'euclidean'`.
+
+Possible metrics are ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, \‘euclidean’, ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, \‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’.
+
+#### How to read the linkage matrix
+
+Z[i] will tell us which clusters were merged in the i-th iteration.
+
+Z[0] with an output array([ 52. , 53. , 0.04151, 2. ])
+
+In its first iteration the linkage algorithm decided to merge the two clusters (original samples here) with indices 52 and 53, as they only had a distance of 0.04151. This created a cluster with a total of 2 samples. We can see that each row of the resulting array has the format idx1, idx2, dist, sample_count.
\ No newline at end of file
diff --git a/script/NLP_clustering_sections.ipynb b/script/NLP_clustering_sections.ipynb
index f788fe0..773d4fc 100644
--- a/script/NLP_clustering_sections.ipynb
+++ b/script/NLP_clustering_sections.ipynb
@@ -27,7 +27,13 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\n/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\n/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\n/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\n/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n return f(*args, **kwds)\n"
+ }
+ ],
"source": [
"# Import basic packages\n",
"import pandas as pd\n",
@@ -37,7 +43,7 @@
"import matplotlib as mpl\n",
"from sklearn.metrics.pairwise import linear_kernel # cosine_similarity as linear_kernel#\n",
"from scipy.cluster.hierarchy import dendrogram, linkage\n",
- "# from scipy.cluster.hierarchy import ward, dendrogram # Ward's method for hierarchical clustering\n",
+ "import pyDigest\n",
"%matplotlib inline "
]
},
@@ -47,7 +53,7 @@
"metadata": {},
"outputs": [],
"source": [
- "# Load dataframe\n",
+ "# Load dataframes\n",
"path_doc = '/home/mribary/Dropbox/pyDigest/dump/D_doc_sections_001.csv'\n",
"path_tfidf = '/home/mribary/Dropbox/pyDigest/dump/D_tfidf_sections_001.csv'\n",
"df = pd.read_csv(path_doc, index_col=0) # Sections with documents inlcuding lemmas from text\n",
@@ -82,87 +88,90 @@
"cell_type": "code",
"execution_count": 4,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": "(432, 6286)"
+ },
+ "metadata": {},
+ "execution_count": 4
+ }
+ ],
"source": [
"X = np.array(top_tf.values) # Tfidf matrix of shape 432 (sections) x 6286 (terms)\n",
- "# X.shape"
+ "X.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": "0 ward & euclidean - matrix ready\n1 single & braycurtis - matrix ready\n2 single & canberra - matrix ready\n3 single & chebyshev - matrix ready\n4 single & cityblock - matrix ready\n5 single & dice - matrix ready\n6 single & euclidean - matrix ready\n7 single & hamming - matrix ready\n8 single & jaccard - matrix ready\n9 single & kulsinski - matrix ready\n10 single & matching - matrix ready\n11 single & minkowski - matrix ready\n12 single & rogerstanimoto - matrix ready\n13 single & russellrao - matrix ready\n14 single & seuclidean - matrix ready\n15 single & sokalmichener - matrix ready\n16 single & sokalsneath - matrix ready\n17 single & sqeuclidean - matrix ready\n18 complete & braycurtis - matrix ready\n19 complete & canberra - matrix ready\n20 complete & chebyshev - matrix ready\n21 complete & cityblock - matrix ready\n22 complete & dice - matrix ready\n23 complete & euclidean - matrix ready\n24 complete & hamming - matrix ready\n25 complete & jaccard - matrix ready\n26 complete & kulsinski - matrix ready\n27 complete & matching - matrix ready\n28 complete & minkowski - matrix ready\n29 complete & rogerstanimoto - matrix ready\n30 complete & russellrao - matrix ready\n31 complete & seuclidean - matrix ready\n32 complete & sokalmichener - matrix ready\n33 complete & sokalsneath - matrix ready\n34 complete & sqeuclidean - matrix ready\n35 average & braycurtis - matrix ready\n36 average & canberra - matrix ready\n37 average & chebyshev - matrix ready\n38 average & cityblock - matrix ready\n39 average & dice - matrix ready\n40 average & euclidean - matrix ready\n41 average & hamming - matrix ready\n42 average & jaccard - matrix ready\n43 average & kulsinski - matrix ready\n44 average & matching - matrix ready\n45 average & minkowski - matrix ready\n46 average & rogerstanimoto - matrix ready\n47 average & russellrao - matrix ready\n48 average & seuclidean - matrix ready\n49 average & sokalmichener - matrix ready\n50 average & sokalsneath - matrix ready\n51 average & sqeuclidean - matrix ready\n52 weighted & braycurtis - matrix ready\n53 weighted & canberra - matrix ready\n54 weighted & chebyshev - matrix ready\n55 weighted & cityblock - matrix ready\n56 weighted & dice - matrix ready\n57 weighted & euclidean - matrix ready\n58 weighted & hamming - matrix ready\n59 weighted & jaccard - matrix ready\n60 weighted & kulsinski - matrix ready\n61 weighted & matching - matrix ready\n62 weighted & minkowski - matrix ready\n63 weighted & rogerstanimoto - matrix ready\n64 weighted & russellrao - matrix ready\n65 weighted & seuclidean - matrix ready\n66 weighted & sokalmichener - matrix ready\n67 weighted & sokalsneath - matrix ready\n68 weighted & sqeuclidean - matrix ready\n69 centroid & euclidean - matrix ready\n70 median & euclidean - matrix ready\n0.19541725126458748\n0.465799116530436\n-0.388969392064966\n0.3028765556939608\n-0.40422515572474715\n0.45702854988462743\n0.5008994958011028\n-0.3891935129698052\n/home/mribary/.local/lib/python3.7/site-packages/scipy/cluster/hierarchy.py:1699: RuntimeWarning: invalid value encountered in double_scalars\n c = numerator.sum() / np.sqrt((denomA.sum() * denomB.sum()))\nno score\n0.49758571101670224\n-0.3891935129698052\n0.5008994958011027\n-0.38663708484366555\n0.49661482620230424\n0.09235447339900012\n-0.38663708484366555\n0.48605887664008335\n0.5033317612294534\n0.4844857262686113\n-0.3404264824428865\n0.2269504729156633\n-0.14560653845298654\n0.4707536479315239\n0.4972862012650147\n-0.36997079348416734\nno score\n0.41724183317258573\n-0.36997079348416734\n0.49728620126501494\n-0.34055626315660903\n0.427651054569636\n0.10270548744187855\n-0.34055626315660903\n0.46629404562829746\n0.5017046890586905\n0.5650119939167992\n-0.3867833590371011\n0.28275107426969226\n-0.30516822239444585\n0.4972004984782347\n0.6829207838359185\n-0.38957466157925325\nno score\n0.5025712811870334\n-0.38957466157925325\n0.6829207838359184\n-0.3851818944409728\n0.5000883802991699\n0.10652169161311566\n-0.3851818944409728\n0.5131182490906622\n0.6810207697882871\n0.4915237709554716\n-0.35248606089352136\n0.26505039525503793\n-0.24712689137478408\n0.46575415767830336\n0.6032541179525794\n-0.38443240701903103\nno score\n0.49819831747475724\n-0.38443240701903103\n0.6032541179525798\n-0.33308512336959095\n0.4774362682362585\n0.1084023628818872\n-0.33308512336959095\n0.4708773665100691\n0.6085681832306272\n0.5821612977691865\n0.44870483191175126\n"
+ }
+ ],
"source": [
- "# Generate linkage matrices with all available methods\n",
- "Z_ward = linkage(X, method='ward') # Uses the Ward variance minimization algorithm\n",
- "Z_single = linkage(X, method='single') # Nearest Point algorithm\n",
- "Z_complete = linkage(X, method='complete') # Farthest Point Algorithm or Voor Hees Algorithm\n",
- "Z_average = linkage(X, method='average') # UPGMA algorithm\n",
- "Z_weighted = linkage(X, method='weighted') # WPGMA algorithm\n",
- "Z_centroid = linkage(X, method='centroid') # Euclidean distance between the centroid and the centroid of a remaining cluster\n",
- "Z_median = linkage(X, method='median') # Merged clusters' centroid to be come the average"
+ "mmdf = pyDigest.linkage_for_clustering(X)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": " method metric CCC_score CCC_abs_score\n73 average euclidean 0.682921 0.682921\n80 average minkowski 0.682921 0.682921\n86 average sqeuclidean 0.681021 0.681021\n108 weighted sqeuclidean 0.608568 0.608568\n102 weighted minkowski 0.603254 0.603254\n95 weighted euclidean 0.603254 0.603254\n117 centroid euclidean 0.582161 0.582161\n66 average braycurtis 0.565012 0.565012\n85 average sokalsneath 0.513118 0.513118\n42 single sqeuclidean 0.503332 0.503332\n77 average kulsinski 0.502571 0.502571\n64 complete sqeuclidean 0.501705 0.501705\n29 single euclidean 0.500899 0.500899\n36 single minkowski 0.500899 0.500899\n82 average russellrao 0.500088 0.500088",
+ "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>method</th>\n <th>metric</th>\n <th>CCC_score</th>\n <th>CCC_abs_score</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>73</th>\n <td>average</td>\n <td>euclidean</td>\n <td>0.682921</td>\n <td>0.682921</td>\n </tr>\n <tr>\n <th>80</th>\n <td>average</td>\n <td>minkowski</td>\n <td>0.682921</td>\n <td>0.682921</td>\n </tr>\n <tr>\n <th>86</th>\n <td>average</td>\n <td>sqeuclidean</td>\n <td>0.681021</td>\n <td>0.681021</td>\n </tr>\n <tr>\n <th>108</th>\n <td>weighted</td>\n <td>sqeuclidean</td>\n <td>0.608568</td>\n <td>0.608568</td>\n </tr>\n <tr>\n <th>102</th>\n <td>weighted</td>\n <td>minkowski</td>\n <td>0.603254</td>\n <td>0.603254</td>\n </tr>\n <tr>\n <th>95</th>\n <td>weighted</td>\n <td>euclidean</td>\n <td>0.603254</td>\n <td>0.603254</td>\n </tr>\n <tr>\n <th>117</th>\n <td>centroid</td>\n <td>euclidean</td>\n <td>0.582161</td>\n <td>0.582161</td>\n </tr>\n <tr>\n <th>66</th>\n <td>average</td>\n <td>braycurtis</td>\n <td>0.565012</td>\n <td>0.565012</td>\n </tr>\n <tr>\n <th>85</th>\n <td>average</td>\n <td>sokalsneath</td>\n <td>0.513118</td>\n <td>0.513118</td>\n </tr>\n <tr>\n <th>42</th>\n <td>single</td>\n <td>sqeuclidean</td>\n <td>0.503332</td>\n <td>0.503332</td>\n </tr>\n <tr>\n <th>77</th>\n <td>average</td>\n <td>kulsinski</td>\n <td>0.502571</td>\n <td>0.502571</td>\n </tr>\n <tr>\n <th>64</th>\n <td>complete</td>\n <td>sqeuclidean</td>\n <td>0.501705</td>\n <td>0.501705</td>\n </tr>\n <tr>\n <th>29</th>\n <td>single</td>\n <td>euclidean</td>\n <td>0.500899</td>\n <td>0.500899</td>\n </tr>\n <tr>\n <th>36</th>\n <td>single</td>\n <td>minkowski</td>\n <td>0.500899</td>\n <td>0.500899</td>\n </tr>\n <tr>\n <th>82</th>\n <td>average</td>\n <td>russellrao</td>\n <td>0.500088</td>\n <td>0.500088</td>\n </tr>\n </tbody>\n</table>\n</div>"
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ],
"source": [
- "'''\n",
- "methods = ['ward', 'single', 'complete', 'average', 'weighted', 'centroid', 'median']\n",
- "metrics = ['euclidean', 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'jensenshannon', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']\n",
- "tuples = []\n",
- "for x in metrics:\n",
- " d = [(k, x) for k in methods]\n",
- " tuples.extend(d)\n",
- "len(tuples)\n",
- "'''"
+ "mmdf"
]
},
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Default metric is 'euclidean'.\n",
- "\n",
- "Possible metrics are ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, \\‘euclidean’, ‘hamming’, ‘jaccard’, ‘jensenshannon’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, \\‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’.\n",
- "\n",
- "#### How to read the linkage matrix\n",
- "\n",
- "`Z[i]` will tell us which clusters were merged in the i-th iteration. \n",
- "\n",
- "```python\n",
- ">>> Z[0] with an output\n",
- "array([ 52. , 53. , 0.04151, 2. ])\n",
- "```\n",
- "\n",
- "In its first iteration the linkage algorithm decided to merge the two clusters (original samples here) with indices 52 and 53, as they only had a distance of 0.04151. This created a cluster with a total of 2 samples. We can see that each row of the resulting array has the format [idx1, idx2, dist, sample_count]."
- ],
- "execution_count": null,
- "outputs": []
- },
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
- "output_type": "stream",
- "name": "stdout",
- "text": "0.19541725126458756\n0.5008994958011028\n0.49728620126501494\n0.6829207838359184\n0.6032541179525797\n0.5821612977691866\n0.4487048319117512\n"
+ "output_type": "display_data",
+ "data": {
+ "text/plain": "<Figure size 1080x1440 with 1 Axes>",
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+ },
+ "metadata": {
+ "needs_background": "light"
+ }
}
],
"source": [
- "# Calculate Cophenetic Correlation Coefficient for the different linkage types\n",
- "from scipy.cluster.hierarchy import cophenet\n",
- "from scipy.spatial.distance import pdist\n",
- "linkage_matrices = [Z_ward, Z_single, Z_complete, Z_average, Z_weighted, Z_centroid, Z_median]\n",
- "# linkage_dict = {'ward':[], 'single':[], 'complete':[], 'average':[], 'weighted':[], 'centroid':[], 'median':[]}\n",
- "for Z in linkage_matrices:\n",
- " c, coph_dists = cophenet(Z, pdist(X))\n",
- " print(c)"
+ "# Create dendogram for hierarchical clustering with top linkage combination\n",
+ "linkage_matrix = linkage(X, method='average', metric='euclidean') # Linkage_matrix using Ward's method\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(15, 20)) # set size\n",
+ "ax = dendrogram(linkage_matrix, orientation=\"right\", labels=section_IDs)\n",
+ "\n",
+ "plt.tick_params(\\\n",
+ " axis= 'x', # changes apply to the x-axis\n",
+ " which='both', # both major and minor ticks are affected\n",
+ " bottom='off', # ticks along the bottom edge are off\n",
+ " top='off', # ticks along the top edge are off\n",
+ " labelbottom='off')\n",
+ "\n",
+ "plt.tight_layout() #show plot with tight layout\n",
+ "\n",
+ "#uncomment below to save figure\n",
+ "plt.savefig('avg_euc_clusters.png', dpi=200) #save figure as ward_clusters"
]
},
{
@@ -171,8 +180,7 @@
"metadata": {},
"outputs": [],
"source": [
- "'''\n",
- "# Create dendogram for hierarchical clustering with Ward's method\n",
+ "'''# Create dendogram for hierarchical clustering with Ward's method\n",
"from scipy.cluster.hierarchy import dendrogram, ward\n",
"\n",
"cosine_sim = linear_kernel(X, X) # Generate cosine similarity matrix: cosine_sim\n",
diff --git a/script/avg_euc_clusters.png b/script/avg_euc_clusters.png
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--
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